Computer Simulation Models

The Silicon Universe: A Master Catalogue of Computer Simulation Models Across Every Field

Published on October 19, 2026 | Computational Science, Simulation, Modelling, Engineering and Digital Twins

Computer simulation is not confined to supercomputers, research laboratories or large engineering companies. Thousands of models run on ordinary laptops, desktop computers, mobile devices, web browsers and small servers. Others are deployed on research clusters, cloud infrastructure and the world's largest supercomputers.

Simulation allows researchers and developers to investigate systems that are too large, too small, too slow, too fast, too dangerous, too expensive or too complicated to study directly. A model might reproduce the behaviour of a single cell, an electrical circuit, an individual investor, a city's traffic, an aircraft, an ecosystem, a national economy or the evolution of the universe.

This catalogue covers computer simulation across the sciences, engineering, medicine, economics, social sciences, transportation, environmental research, manufacturing, computing, artificial intelligence and many other fields. It includes open-source software, academic research projects, established commercial platforms, educational simulations and repositories containing individual model implementations.

The objective is to provide a broad starting point for discovering simulation models regardless of computing requirements. The catalogue is not restricted to software capable of running on supercomputers.

Understanding Simulation Models and Software

The term "computer simulation model" covers several related but different things. Understanding the distinction makes it much easier to discover relevant research and software.

Mathematical and conceptual models

A model specifies how a system behaves. It may consist of differential equations, probability distributions, physical laws, decision rules, state transitions, a network of relationships, or a collection of interacting agents. Some models are simple enough to express in a few lines of Python; others require complex numerical solvers.

Simulation engines and modelling platforms

A simulation engine executes a model. Examples include OpenFOAM for fluid dynamics, NetLogo for agent-based modelling, SimPy for discrete-event processes and OpenModelica for equation-based physical systems. A platform may support thousands of distinct models rather than one particular simulation.

Individual implementations and research models

An individual model is a specific implementation of an idea, hypothesis or research method. It might reproduce a published paper, simulate one biological pathway, represent one city, test a proposed economic mechanism or model one particular material. Such models are frequently distributed through GitHub, academic repositories, supplementary research files and specialist model libraries.

Computational requirements

A simulation's hardware requirements depend on model size, resolution, accuracy, simulation duration, number of agents, number of scenarios and computational method. The same broad problem may have a small desktop implementation and a more elaborate high-performance implementation. Most simulation fields contain examples that do not require supercomputers.

The lists below therefore include both individual model families and software used to create or execute them. Not every listed product is open source, and not every software package includes a ready-made model for every example described.

1. Repositories Containing Actual Simulation Models

The following repositories and directories are particularly valuable when the goal is to find existing models rather than simply identify modelling software. Some preserve source code directly; others catalogue models and link to their code, documentation, data or publications.

General-purpose code discovery

Academic and specialist model libraries

  • CoMSES Net Computational Model Library: Published computational models, particularly in the social and ecological sciences. Model pages provide descriptions and research metadata; eligible models can undergo peer review and receive DOIs.
  • Physiome Model Repository: Reusable physiological and biological models, including representations of organ systems, cells and other biological processes.
  • CellML Model Repository: Mathematical biological models covering categories such as cardiovascular circulation, electrophysiology, metabolism, gene regulation, ion transport and signal transduction.
  • ModelDB: Computational neuroscience models covering neurons, synapses, ion channels, realistic networks and neural pathophysiology. The repository accepts models written for different programming languages and simulation environments.
  • Open Source Physics: Physics teaching models, computational experiments and resources for learning, modifying and creating simulations.
  • USGS Scientific Model Catalog: A catalogue linking scientific models with documentation, publications, software, data and applications, including groundwater, hydrology, seismology and other Earth-science areas. It is a discovery catalogue rather than a repository containing every model's source code.
  • EPA EcoService Models Library: A searchable library for finding and comparing ecological models used to examine ecosystem processes and ecosystem services.
  • Modelica Libraries: A directory of free and commercial equation-based model libraries for physical and engineering systems, including electrical, thermal, mechanical and energy applications.
  • BioModels Database: Published biological and biochemical mathematical models, including systems described using standard formats such as SBML.
  • Systems Biology Markup Language (SBML): A standard and supporting software ecosystem for representing and exchanging models of biochemical networks and other biological systems.

These sources are complementary. GitHub is useful for code discovery, while specialist academic libraries can provide curation, model metadata, links to publications and information needed to reproduce a study. No single repository contains all simulation models.

2. Mathematics, Numerical Experiments and General-Purpose Simulation

Almost any scientific discipline can use general-purpose numerical tools to create a simulation. The same programming environment can model population growth, heat diffusion, orbital motion, fluid flow, financial risk or chemical reaction rates, provided the relevant equations and methods are implemented.

  • SciPy and NumPy: Numerical computation, integration, optimisation, random sampling, differential equations and scientific experiments in Python.
  • DifferentialEquations.jl: A broad collection of differential-equation solvers for ordinary, stochastic, delay and other dynamical systems in Julia.
  • GNU Octave: Numerical modelling and matrix-oriented scientific computation using a MATLAB-like language.
  • MATLAB and Simulink: Numerical modelling, control-system simulation, signal processing and simulation of interacting physical and engineered systems.
  • Wolfram Mathematica: Symbolic mathematics, numerical modelling, differential equations, optimisation and visual simulation.
  • Jupyter: An environment for combining executable scientific code, explanations, charts and reproducible computational experiments.
  • OpenModelica: An open-source environment for equation-based modelling and simulation of interconnected physical systems.

Example models: predator-prey equations, chaotic systems, orbital mechanics, spring-mass systems, heat diffusion, random walks, epidemic curves, population dynamics, queueing systems and numerical optimisation experiments.

3. System Dynamics and Complex Systems

System-dynamics models represent stocks, flows, feedback loops, delays and causal relationships. They are useful for investigating how a system changes over time, particularly when feedback and accumulated effects matter more than microscopic physical detail.

  • Vensim: System-dynamics modelling for business, public policy, resource management and complex feedback systems.
  • Stella Architect: Diagram-based dynamic modelling and simulation.
  • Insight Maker: Browser-based system dynamics, causal-loop diagrams and agent-based simulation.
  • NetLogo: An accessible environment for creating and exploring models of complex systems, including emergent behaviour among interacting entities.
  • AnyLogic: A commercial modelling platform supporting agent-based, discrete-event and system-dynamics simulation.

Example models: economic cycles, resource depletion, organisational growth, disease progression, technological adoption, feedback in industrial systems, population change and competing species.

4. Agent-Based Simulation and Individual Behaviour

Agent-based models represent individual entities and allow system-wide behaviour to emerge from their interactions. An agent can represent a person, household, company, animal, vehicle, robot or autonomous software process.

  • NetLogo: A free environment for building individual-based simulations of social, ecological, biological and physical systems.
  • Mesa: An open-source Python framework for constructing, visualising and analysing agent-based models.
  • GAMA Platform: An open-source environment for spatial agent-based simulation, including urban systems, mobility, epidemiology and disaster response.
  • Repast: An ecosystem of agent-based modelling tools, including versions aimed at Java, Python and large-scale simulation.
  • MASON: A Java-based agent-based simulation toolkit.
  • AnyLogic: Supports agent-based models alongside system-dynamics and discrete-event methods.

Example models: crowd formation, segregation, consumer decisions, predator-prey interactions, migration, opinion formation, social networks, market behaviour, wildfire spread, epidemic transmission and cooperation or conflict between individuals.

5. Discrete-Event, Queueing and Process Simulation

Discrete-event simulation represents systems in which state changes occur at identifiable events. Instead of calculating every instant of time, a model may jump from one event to the next: an order arrives, a machine finishes a task, a server becomes available or a customer joins a queue.

  • SimPy: A Python framework for modelling processes, resources, queues and discrete events.
  • JaamSim: A discrete-event simulation environment for operations, logistics and industrial processes.
  • AnyLogic: Supports process-centric, discrete-event and hybrid simulations.
  • FlexSim: A commercial platform for factory, warehouse, healthcare and process simulation.
  • Simio: Discrete-event modelling of manufacturing, service operations and supply chains.
  • Arena Simulation: Process and discrete-event simulation for operational analysis.

Example models: hospital waiting rooms, supermarket queues, call centres, production lines, airport security, server capacity, machine maintenance, warehouse picking and emergency-service workloads.

6. Social Science, Demography, Institutions and Public Policy

Social simulation represents households, communities, institutions and human decisions. Models can investigate how individual behaviour and policy choices influence population-level results. These are simplified representations of human systems, not literal replicas of every person's beliefs or intentions.

  • CoMSES Net: An especially useful source of published computational models in social and ecological science.
  • NetLogo: Includes models of social behaviour, opinion dynamics, cooperation, segregation and collective action.
  • Mesa: Python-based models of people, institutions, communities and interacting populations.
  • GAMA: Spatial models of population movement, urban life, evacuation and human-environment interaction.
  • Repast: Agent-based social and organisational modelling.
  • OpenFisca: An open-source microsimulation policy engine used to represent tax-benefit systems and analyse policy scenarios.

Example models: household formation, migration, ageing populations, electoral participation, social influence, segregation, inequality, education systems, policy uptake, taxation, welfare systems and institutional change.

7. Economics, Finance, Business Cycles and Markets

Economic simulations represent relationships between households, firms, banks, governments and markets. Approaches include equilibrium models, macroeconomic time-series models, microsimulation, agent-based economics and stochastic financial models.

  • Dynare: Open-source tools for solving and estimating dynamic stochastic general equilibrium models and other economic systems.
  • QuantLib: Open-source quantitative-finance tools for pricing, risk calculations, yield curves and financial models.
  • ABIDES: An agent-based discrete-event simulation framework designed for studying financial markets and market participants.
  • Econ-ARK: Tools for constructing and analysing heterogeneous-agent economic models.
  • OpenFisca: Microsimulation of tax-benefit rules and policy changes.
  • AnyLogic: Can represent supply, demand, operations, market participants and business processes using multiple simulation paradigms.

Example models: inflation mechanisms, monetary policy, unemployment, housing markets, banking networks, credit risk, portfolio returns, financial derivatives, trading strategies, market microstructure, business failures and the effects of taxation or subsidies.

A simulated market is not a guaranteed forecast of a real market. Outputs depend on the model's assumptions, data, calibration and representation of participant behaviour.

8. Epidemiology, Infectious Diseases and Public Health

Epidemiological models examine how diseases spread through populations and how interventions may alter transmission. Different models represent populations as compartments, networks of contacts, individual agents or spatially distributed populations.

  • Covasim: An open-source agent-based model for simulating infectious disease transmission and intervention scenarios.
  • EpiModel: An R framework for mathematical and network-based infectious disease modelling.
  • GLEAMviz: An epidemiological modelling environment based on population structure, mobility and disease transmission.
  • NetLogo: Suitable for smaller custom models of infection, contact networks and behavioural responses.
  • Mesa: Can be used to implement custom agent-based disease transmission models.
  • CoMSES Net: Useful for discovering published agent-based epidemiology and health-related models.

Example models: SIR and SEIR compartment models, influenza, COVID-19, vector-borne disease, hospital capacity, vaccination strategies, contact tracing, disease importation, population mobility and the effects of social-distancing policies.

9. In Silico Biology, Human Physiology and Medical Simulation

Biological and medical models range from individual molecular interactions to full-body biomechanical systems. They may represent organ function, blood circulation, electrical activity, muscle recruitment, tissue growth or the response of anatomy to mechanical forces.

  • OpenSim: Open-source musculoskeletal modelling and simulation for biomechanics, movement analysis, muscle forces and joint mechanics.
  • AnyBody Modeling System: Commercial musculoskeletal modelling used in biomechanics, ergonomics and orthopaedics.
  • SimVascular: Tools for patient-specific cardiovascular modelling, vascular geometry and blood-flow simulation.
  • Chaste: An open-source framework for computational biology, including tissue, cell-based and cardiac electrophysiology modelling.
  • PhysiCell: An open-source framework for multicellular biological simulation, including cell-cell interactions, microenvironments and tumour modelling.
  • SIMULIA Living Heart Project: A commercial research and engineering initiative focused on virtual heart modelling and cardiac simulation.
  • THUMS and GHBMC human body models: Detailed anatomical models used in biomechanical injury research and vehicle-safety engineering. Access and licensing vary by model.
  • Physiome Model Repository: Reusable models of organ systems and physiological mechanisms.

Example models: cardiac electrophysiology, blood flow, respiratory mechanics, glucose regulation, joint loading, muscle activation, implant biomechanics, tumour growth, tissue regeneration, traumatic injury and virtual patient physiology.

10. Computational Neuroscience and Brain Simulation

Computational neuroscience models electrical and chemical signalling in neurons and networks. Depending on the research question, a model can represent a single ion channel, a detailed neuron, a group of interacting cells or a large neural network.

  • ModelDB: A major discovery resource for published computational neuroscience models.
  • NEURON: Simulates detailed neuronal structures, membrane properties and networks.
  • Brian: An open-source simulator for spiking neural networks.
  • NEST: A simulator for large networks of spiking neurons.
  • NEST Simulator: Supports computational neuroscience experiments involving networks of neurons.
  • The Virtual Brain: A platform for large-scale brain-network modelling and simulation.
  • NetPyNE: Tools for constructing and analysing biological neuronal network models.

Example models: action potentials, synaptic plasticity, neural oscillations, sensory processing, cortical networks, memory mechanisms, epilepsy dynamics and brain-network activity.

11. Systems Biology, Biochemical Networks and Pharmacology

Systems biology models how genes, proteins, metabolites and signalling pathways interact. These models frequently use differential equations, reaction networks, Boolean logic or stochastic processes.

  • COPASI: Modelling and simulation of biochemical networks, reaction systems and cellular processes.
  • CellDesigner: A graphical editor for constructing and visualising biochemical and gene-regulatory network models.
  • BioModels: A searchable database of published mathematical models of biological processes.
  • SBML: Standardised infrastructure for representing and exchanging computational systems-biology models.
  • CellML: A model representation and repository ecosystem for physiological and biological models.
  • Tellurium: Tools for reproducible modelling and simulation of biological networks.

Example models: gene regulation, protein interaction, metabolic networks, biochemical oscillations, cell-cycle control, drug pharmacokinetics, pharmacodynamics, pathway inhibition and synthetic biological circuits.

12. Molecular Dynamics, Protein Behaviour and Biophysics

Molecular simulation investigates atomic interactions and molecular movement using classical, coarse-grained or related computational methods. Some simulations can run on a workstation; larger systems and longer trajectories may benefit from GPUs or HPC resources.

  • GROMACS: Molecular dynamics for proteins, membranes, nucleic acids and other molecular systems.
  • NAMD: Molecular dynamics designed for biomolecular simulation and scalable computation.
  • AMBER: A suite of tools and force fields for molecular dynamics and biomolecular simulation; licensing varies by component.
  • LAMMPS: Molecular dynamics for materials, polymers, metals, soft matter and other particle systems.
  • OpenMM: A toolkit for molecular simulation with programmable workflows and GPU support.
  • MDAnalysis: Tools for analysing molecular dynamics trajectories and simulation data.

Example models: protein-ligand interactions, membrane dynamics, solvent behaviour, molecular conformational changes, polymer deformation, diffusion, biomolecular stability and nanoscale material properties.

13. Quantum Chemistry, Electronic Structure and Materials Science

Quantum-chemical and electronic-structure simulations investigate molecules and materials using quantum mechanics. They can estimate electronic energies, molecular structures, reaction pathways, optical properties and material behaviour.

  • Quantum ESPRESSO: Open-source electronic-structure and materials modelling software, including density-functional-theory calculations.
  • CP2K: Molecular simulation and electronic-structure calculations for molecules, liquids, solids and materials.
  • NWChem: Computational chemistry and electronic-structure modelling.
  • ORCA: Computational chemistry software available under licensing conditions defined by its developer.
  • VASP: Electronic-structure calculations and atomistic simulation for materials research; commercial licensing applies.
  • Gaussian: Computational chemistry software for molecular electronic structure and related calculations; commercial licensing applies.
  • QuTiP: Open-source numerical tools for simulating quantum systems and quantum dynamics.
  • Qiskit: Quantum-computing software with circuit simulation capabilities.

Example models: chemical reaction pathways, catalytic surfaces, battery materials, semiconductor properties, defects in crystals, molecular orbitals, optical response, quantum circuits and quantum-state evolution.

14. Fluid Dynamics, Heat Transfer, Combustion and Acoustics

Computational fluid dynamics (CFD) models how liquids and gases move. Related models investigate temperature, turbulence, combustion, chemical transport, noise and other coupled phenomena. These methods are relevant to both industrial engineering and environmental science.

  • OpenFOAM: Open-source CFD tools for fluid flow, heat transfer, turbulence and multiphysics problems.
  • ANSYS Fluent: Commercial simulation of complex fluid flow, heat transfer, combustion and multiphase systems.
  • Simcenter STAR-CCM+: Commercial engineering simulation for fluid flow, thermal performance and related multiphysics applications.
  • COMSOL Multiphysics: Coupled simulation involving flow, heat, structural mechanics, acoustics, electromagnetics and other physical processes.
  • Elmer: Open-source multiphysics finite-element software with fluid, heat and structural applications.
  • SU2: Open-source computational analysis and design tools, especially useful in fluid dynamics and aerospace engineering.
  • Fire Dynamics Simulator (FDS): Computational modelling of fire-driven fluid flow and smoke transport.

Example models: aircraft aerodynamics, pipe flow, ventilation, engine combustion, wind turbines, ocean flow, heat exchangers, industrial mixing, building airflow, smoke dispersion and aeroacoustics.

15. Structural Mechanics, Finite-Element Analysis and Materials Failure

Structural and solid-mechanics simulations estimate stress, deformation, vibration, fatigue, crack growth, contact and failure. Finite-element analysis (FEA) is a widely used numerical approach, but not the only method for modelling solid materials.

  • LS-DYNA: High-deformation and transient simulations involving impact, crash, contact and material failure.
  • Abaqus: Structural and multiphysics simulation, including nonlinear material behaviour and contact mechanics.
  • ANSYS Mechanical: Structural, thermal, vibration and fatigue simulation.
  • Code_Aster: An open-source structural analysis and multiphysics platform.
  • CalculiX: Free finite-element analysis software for engineering mechanics.
  • OpenSees: Simulation of structural and geotechnical systems, particularly earthquake engineering.
  • MOOSE Framework: An open-source multiphysics framework used for coupled engineering and scientific simulation.
  • Kratos Multiphysics: An open-source framework for developing computational mechanics and multiphysics applications.

Example models: bridges, buildings, turbine blades, vehicle crashes, composite materials, plastic deformation, fatigue, fracture, earthquake response, contact between parts and protective equipment.

16. Electrical Engineering, Electronics and Power Systems

Electrical simulations represent voltage, current, electromagnetic behaviour, electronic components, power networks and control systems. Depending on the model, simulation may involve circuit equations, switching events, electromagnetic fields or grid-level power flow.

  • ngspice: Open-source circuit simulation for analogue, digital and mixed-signal electronic systems.
  • Qucs and Qucs-S: Circuit design and simulation environments.
  • OpenDSS: Electric power distribution-system simulation, including distributed energy resources.
  • pandapower: Python tools for power-system analysis and electrical-network calculations.
  • GridLAB-D: Simulation of electric distribution systems, demand, buildings and smart-grid behaviour.
  • PyPSA: Open-source energy-system modelling and optimisation, including electricity grids and integrated energy systems.
  • OpenModelica: Can simulate electrical circuits and interconnected systems involving electrical, mechanical and thermal components.

Example models: transistor circuits, amplifiers, filters, control loops, power converters, batteries, photovoltaic systems, distribution grids, power-flow stability, microgrids and energy-storage dispatch.

17. Robotics, Autonomous Machines and Physical Interaction

Robotics simulators let developers test robot designs, sensors, controllers and autonomous behaviour without requiring a physical robot for every experiment. They can model rigid-body motion, contact, sensors, actuators and virtual environments.

  • Gazebo: A robotics simulator used with robotic software frameworks and virtual environments.
  • Webots: A robot simulator for prototyping, education and research.
  • MuJoCo: Physics simulation for articulated bodies, robotics and control research.
  • NVIDIA Isaac Sim: A robotics simulation platform supporting synthetic data generation and virtual robot testing.
  • CoppeliaSim: A robotics simulator for robot mechanisms, sensor models, control and automation.
  • PyBullet: Python bindings for physics simulation, often used in robotics and reinforcement-learning experiments.
  • Project Chrono: Multibody dynamics and physical simulation for mechanical systems and vehicles.

Example models: robotic arms, humanoid movement, walking robots, drone navigation, manipulation, autonomous control, multi-robot coordination, sensor fusion and collision avoidance.

18. Transportation, Road Traffic, Public Transit and Pedestrian Flow

Transport simulation ranges from individual vehicles and pedestrians to city-wide mobility systems. Microsimulation represents individual road users, while macroscopic models represent aggregate traffic flow. Agent-based models can additionally represent travellers making route and activity choices.

  • SUMO: Open-source microscopic traffic simulation supporting roads, vehicles, traffic signals and multiple modes of transport.
  • MATSim: An open-source framework for large-scale agent-based transport simulations, including individual travel plans and mobility patterns.
  • PTV Vissim: Commercial microscopic traffic and transport simulation.
  • Aimsun: Traffic modelling and transport planning software.
  • TransModeler: Traffic simulation and transportation analysis.
  • GAMA: Can represent mobility, spatial movement, pedestrian evacuation and urban systems.
  • AnyLogic: Supports traffic, passenger, pedestrian and logistics models.

Example models: traffic congestion, road intersections, traffic-light optimisation, motorway flow, bus networks, railway scheduling, pedestrian crowd dynamics, evacuation routes, cycling infrastructure and the impact of new transport policies.

19. Aerospace, Aviation, Flight Dynamics and Spaceflight

Aerospace simulation models vehicle motion, aerodynamics, propulsion, guidance, navigation, control and orbital dynamics. Some are desktop tools for learning and testing; others support engineering validation and mission analysis.

  • FlightGear: An open-source flight simulator.
  • JSBSim: An open-source flight-dynamics model and simulation library.
  • SU2: Computational fluid dynamics and design tools with aerospace applications.
  • NASA General Mission Analysis Tool (GMAT): Spacecraft trajectory and mission-design simulation.
  • Orekit: An open-source library for space-flight dynamics and related calculations.
  • Project Pluto: Astronomical and orbital computation tools.
  • OpenMDAO: An open-source framework for multidisciplinary analysis and optimisation, including aerospace engineering.

Example models: aircraft flight characteristics, autopilots, engine performance, orbital transfers, satellite constellations, spacecraft attitude, launch trajectories, re-entry, aerodynamic optimisation and mission planning.

20. Weather Forecasting, Climate Change and Atmospheric Science

Atmospheric models represent motion, energy, moisture, radiation, clouds and interactions with Earth's surface. Weather prediction concerns shorter timescales, while climate models investigate long-term statistics and interactions among the atmosphere, oceans, land and cryosphere.

Example models: thunderstorms, hurricanes, monsoons, rainfall, drought, heat waves, atmospheric chemistry, aerosol transport, regional climate, sea-ice interactions, carbon cycles and long-term climate scenarios.

21. Oceanography, Hydrology, Rivers and Groundwater

Water-resource models represent precipitation, runoff, rivers, floodplains, groundwater, reservoirs, water quality and marine processes. They support scientific research as well as engineering, water management and hazard assessment.

  • HEC-RAS: River hydraulics, floodplain and related hydraulic modelling.
  • HEC-HMS: Watershed hydrology and rainfall-runoff simulation.
  • USGS Water Resources Software: A gateway to scientific software and models involving surface water, groundwater and water quality.
  • MODFLOW: Groundwater-flow modelling software from the USGS.
  • Deltares software: A source for water, coastal, flood, hydraulic and environmental modelling software, including Delft3D resources.
  • TELEMAC-MASCARET: Open-source numerical modelling of free-surface flows and related environmental processes.
  • Soil and Water Assessment Tool (SWAT): Watershed-scale simulation of hydrology, land management and water quality.

Example models: floods, stormwater drainage, river hydraulics, reservoir operations, groundwater contamination, aquifer recharge, coastal circulation, sediment movement, estuary dynamics, irrigation and water-quality change.

22. Geology, Seismology, Earthquakes and Subsurface Systems

Geophysical models investigate the Earth's structure and response to physical processes. They may represent seismic-wave propagation, rock deformation, groundwater, heat flow, subsurface fluids and geological hazards.

Example models: earthquake rupture, seismic-wave propagation, landslides, volcanic processes, tectonic deformation, geothermal reservoirs, carbon storage, petroleum reservoirs, contaminant migration and subsurface thermal processes.

23. Ecology, Wildlife, Fisheries, Agriculture and Natural Resources

Environmental models examine interactions among organisms, ecosystems, land, water, climate and human activity. Some represent individual organisms; others model populations, food webs, crop growth or ecosystem processes.

  • EPA EcoService Models Library: Discovery and comparison of ecological models used to assess ecosystem goods and services.
  • CoMSES Net: Published social-ecological and agent-based models.
  • InVEST: Ecosystem-service modelling tools, including applications involving habitat, water, carbon and land-use decisions.
  • APSIM: Agricultural systems modelling, including crops, soils, climate and management.
  • AquaCrop: Crop-water productivity modelling developed by the Food and Agriculture Organization.
  • Ecopath with Ecosim: Food-web and ecosystem modelling, particularly useful in aquatic systems and fisheries.
  • NetLogo: Suitable for custom models of predator-prey relationships, vegetation, animal behaviour and resource use.
  • USGS Model Catalog: Discovery of environmental, ecological and natural-resource models.

Example models: forest growth, habitat fragmentation, wildlife populations, invasive species, fisheries, pollination, crop yield, soil erosion, nutrient cycles, carbon storage, ecosystem restoration, drought stress and biodiversity change.

24. Urban Planning, Geography, Land Use and Spatial Simulation

Spatial simulation models changes over geographic space. These models can combine GIS data, transport, housing, population, land use, economic activity and environmental factors to explore development scenarios.

  • UrbanSim: Urban development and land-use modelling, including interactions among households, employment, buildings and transport.
  • QGIS: Open-source geographic information software that supports spatial analysis and workflows incorporating models.
  • GRASS GIS: Open-source geospatial analysis, environmental modelling and raster-based computation.
  • MATSim: Agent-based transport modelling that can be used as part of urban and regional scenario analysis.
  • GAMA: Spatially explicit agent-based simulation for urban systems, mobility and land-use-related questions.
  • CoMSES Net: A source of research models involving spatial and social-ecological systems.

Example models: urban expansion, housing supply, population migration, land-use change, commuting, service accessibility, zoning scenarios, urban heat islands, spatial inequality and the effects of infrastructure investment.

25. Buildings, Architecture, Indoor Environments and Energy Efficiency

Building simulation examines how structures interact with weather, sunlight, occupants, ventilation systems, heating, cooling and energy supply. These models support architectural design, retrofit planning and energy-system integration.

  • EnergyPlus: Building energy and environmental simulation, including heating, cooling, ventilation and energy consumption.
  • OpenStudio: Open-source tools and workflows for building energy modelling, including EnergyPlus integration.
  • Modelica Libraries: Libraries for modelling building systems, HVAC, thermal behaviour and energy interactions.
  • Radiance: Lighting and daylight simulation.
  • COMSOL Multiphysics: Can model building-related heat transfer, airflow, structural behaviour and other coupled processes.
  • AnyLogic: Useful for representing occupants, facility operations and energy-related processes in larger system models.

Example models: energy consumption, passive cooling, insulation performance, indoor air quality, daylight, thermal comfort, heat pumps, occupancy, building-integrated solar power and district heating.

26. Manufacturing, Factories, Warehouses and Supply Chains

Industrial simulations test how production systems operate under different demand levels, machine capacities, maintenance schedules, warehouse layouts and supply constraints. They are useful for finding bottlenecks without repeatedly interrupting real operations.

  • AnyLogic: Simulation of manufacturing, warehouses, distribution networks and supply chains using several modelling approaches.
  • FlexSim: Discrete-event simulation for production, warehousing, material handling and healthcare operations.
  • Simio: Process and manufacturing simulation, including complex operational systems.
  • JaamSim: Discrete-event and process simulation for operational planning.
  • SimPy: A flexible programming framework for custom production, queueing and logistics models.
  • OpenModelica: Equation-based simulation of machinery, process equipment and interacting physical systems.

Example models: assembly lines, machine utilisation, production scheduling, maintenance, just-in-time inventory, factory layout, robotics integration, distribution centres, warehouse automation, delivery fleets and supply-chain disruption.

27. Computer Networks, Telecommunications and Distributed Systems

Computer-system simulation investigates how software and infrastructure behave under load, failures, changing traffic patterns and different network designs. Such simulations are useful when direct experiments are expensive, disruptive or difficult to reproduce.

  • ns-3: An open-source discrete-event network simulator used in networking research.
  • OMNeT++: A modular discrete-event simulation framework for communication networks and other distributed systems.
  • Mininet: Creates virtual network topologies for prototyping and testing networking software; it emulates hosts and switches rather than replacing every type of physical-network simulation.
  • SimGrid: Simulation of distributed applications, computing platforms and resource-management strategies.
  • CloudSim: A simulation framework for cloud-computing infrastructure, resource allocation and service provisioning.
  • gem5: Computer-system architecture simulation, including processor and memory-system research.
  • SST: Simulation tools for studying large-scale computing systems and high-performance computing architectures.

Example models: packet routing, network congestion, wireless communication, Internet protocols, data-centre scheduling, cloud resource allocation, distributed algorithms, processor architecture, memory hierarchies and network resilience.

28. Games, Interactive Environments and Real-Time Physics

Game engines and physics libraries frequently contain simulation capabilities that are useful well beyond entertainment. They can represent rigid bodies, collisions, vehicles, fluids, character movement and interactive environments. The level of physical fidelity varies considerably.

  • Godot: An open-source game engine with physics, animation and interactive-world capabilities.
  • Unity: A game and real-time 3D development engine used for interactive simulations and virtual environments.
  • Unreal Engine: A real-time 3D engine used in interactive simulation, visualisation and training.
  • Box2D: A 2D rigid-body physics engine.
  • Bullet Physics: Physics simulation for rigid bodies and related interactive applications.
  • Project Chrono: Multibody and physical-system simulation, including vehicles and mechanical interaction.

Example models: rigid-body collisions, vehicle dynamics, virtual laboratories, interactive physics demonstrations, character movement, realistic object handling, flight training, machinery visualisation and interactive emergency scenarios.

A visually convincing environment is not automatically a scientifically accurate simulation. The physics, numerical methods and validation depend on the particular application.

29. Astronomy, Astrophysics and Cosmology

Astrophysical simulations investigate gravitational interactions, stars, galaxies, gas dynamics, black holes, dark matter and the large-scale structure of the universe. The scale of computation varies from a simple orbital model on a laptop to simulations spanning cosmological volumes.

  • GADGET: N-body and hydrodynamical simulation used in astrophysical research.
  • Enzo: An astrophysical simulation code for cosmological gas dynamics and related processes.
  • FLASH: A multiphysics simulation code used in astrophysics and other high-energy-density research.
  • MESA: A widely used software suite for stellar evolution and related astrophysical calculations.
  • REBOUND: An N-body simulation package particularly useful for gravitational dynamics and planetary systems.
  • AMUSE: The Astrophysical Multipurpose Software Environment, designed to combine astrophysical simulation codes.

Example models: planetary orbits, star formation, stellar evolution, supernovae, galaxy collisions, accretion discs, gravitational dynamics, black-hole systems, dark-matter structure and cosmological evolution.

30. Plasma Physics, Nuclear Energy and Fusion

Plasma models investigate ionised matter under electromagnetic forces. Fusion research, space physics and high-energy-density experiments may require several different mathematical descriptions, including magnetohydrodynamics, kinetic particle methods and fluid models.

  • BOUT++: Plasma turbulence simulation, including applications to magnetically confined plasmas.
  • Fusion research resources: A starting point for discovering current fusion modelling projects and specialised software.
  • Vlasiator: A hybrid-Vlasov simulation code used for space-plasma research.
  • Oak Ridge National Laboratory: A research source for nuclear-energy, reactor physics and computational-science modelling projects.
  • LAMMPS: Can support atomistic and materials models relevant to radiation damage and other energy-materials research when appropriate models and methods are used.
  • FLASH: Includes multiphysics capabilities used in high-energy-density physics research.

Example models: plasma turbulence, particle transport, magnetohydrodynamics, magnetic confinement, space plasma, solar wind, radiation-material interactions and selected reactor-physics problems.

Fusion and nuclear applications use specialised codes with different availability, licensing and computational requirements. The examples above are discovery starting points rather than an exhaustive inventory of reactor-physics software.

31. Fire, Smoke, Evacuation and Natural-Disaster Simulation

Disaster models help investigate hazards and the movement of people, fire, smoke, water or debris. Different models often need to be coupled to evaluate the complete event, since no single program necessarily represents every physical and human process.

  • Fire Dynamics Simulator and Smokeview: Fire-driven fluid flow, heat transfer and smoke transport.
  • USGS Model Catalog: Discoverability for scientific models relating to earthquakes, landslides, flooding, hazards and Earth processes.
  • Global Earthquake Model resources: Tools and models used in seismic-risk and loss assessment.
  • GAMA: Spatial agent-based scenarios involving evacuation, human movement and emergency management.
  • NetLogo: Useful for small custom models of evacuation behaviour and interacting populations.
  • HEC-RAS: Hydraulic and floodplain modelling.
  • AnyLogic: Can combine process models, agent behaviour and operational resource constraints in emergency scenarios.

Example models: building fires, smoke spread, wildfire propagation, crowd evacuation, flood inundation, landslides, earthquake consequences, emergency-response logistics, shelter capacity and cascading infrastructure failures.

32. Military, Defence, Strategic and Operations Research Simulation

Defence and strategic simulation can represent logistics, force movement, sensor coverage, resource allocation, command decisions and interactions among competing entities. Models may range from abstract tabletop-style systems to detailed training environments.

  • AnyLogic: Can represent complex logistics, resource allocation, queues and operational processes.
  • CoMSES Net: Includes computational social-science models that can be relevant to cooperation, conflict, institutions and collective behaviour.
  • NetLogo: Useful for abstract strategic and agent-based models, including resource competition and movement.
  • MATSim: Transport and network simulation that may be relevant to logistics and movement scenarios.
  • SimPy: A general framework for custom discrete-event simulations of supply, maintenance, scheduling and resource capacity.

Example models: logistics, fleet maintenance, resource allocation, convoy scheduling, communications infrastructure, supply resilience, force-on-force abstractions, strategic decision-making and training scenarios.

Many specialised military simulation products are proprietary, restricted or unavailable to the public. The tools above are general modelling environments rather than substitutes for every specialised defence system.

33. Digital Twins and Cyber-Physical Systems

A digital twin is a digital representation of a physical asset, process or system that can be linked with data from its real-world counterpart. It may combine simulation with sensor data, monitoring, analytics and control. Not every digital twin contains a physics-based simulation, and not every simulation is a digital twin.

  • OpenModelica: Equation-based simulation for interconnected physical systems.
  • Modelica Libraries: Reusable models for machines, vehicles, buildings, energy systems and physical processes.
  • COMSOL Multiphysics: Coupled physical simulation that can form part of a digital-twin workflow.
  • Ansys simulation products: Engineering modelling and simulation products used in digital-engineering workflows.
  • NVIDIA Omniverse: A platform for connecting and developing 3D workflows, simulation applications and industrial digital-twin environments.
  • EnergyPlus and OpenStudio: Building simulation technologies that can support energy-related digital twins.

Example models: factories, power plants, industrial machines, vehicle fleets, buildings, warehouses, power networks, water systems, medical devices and city infrastructure.

34. Monte Carlo Simulation, Risk Analysis and Uncertainty Modelling

Monte Carlo methods use repeated random sampling to estimate possible outcomes, probabilities, uncertainty and risk. They can be applied to finance, engineering, medicine, reliability, statistics, physics and decision analysis without requiring a specialised simulation engine.

  • NumPy: Random sampling and numerical building blocks for Monte Carlo experiments in Python.
  • SciPy: Statistical distributions, numerical integration and scientific computing tools that support uncertainty analysis.
  • QuantLib: Quantitative-finance algorithms and instruments, including methods useful in financial pricing and risk modelling.
  • OpenMDAO: Multidisciplinary analysis and optimisation workflows that can be combined with uncertainty-analysis methods.
  • AnyLogic: Supports repeated runs and scenario-based analysis of operational models.

Example models: portfolio risk, project-cost uncertainty, equipment reliability, component failure, disease uncertainty, engineering tolerances, inventory risk, insurance claims, queueing variability and scenario planning.

Monte Carlo simulation is a method rather than one particular domain. It can be added to a model in almost any of the other categories in this catalogue.

35. Artificial Intelligence, Reinforcement Learning and Synthetic Environments

AI research often relies on interactive simulated environments in which an agent observes a state, selects an action and receives a result. The environment may reproduce a physical task, traffic conditions, robot movement, a strategy game or an abstract decision process.

  • Gymnasium: A standard API and collection of environments for reinforcement-learning research.
  • MuJoCo: Physics simulation frequently used for robotics, motor control and reinforcement-learning tasks.
  • NVIDIA Isaac Sim: Robotics simulation and synthetic-data workflows.
  • CARLA: An open-source simulator designed for autonomous-driving research.
  • Habitat: An ecosystem for embodied AI and interactive environment research.
  • NetLogo: Can be used to create small decision environments and multi-agent experiments.
  • Mesa: A Python environment for building custom interactive agent-based simulations that can be combined with AI methods.

Example models: robotic manipulation, autonomous driving, vehicle control, navigation, warehouse automation, resource scheduling, multi-agent coordination, game strategies and policy learning.

Simulated success does not automatically imply real-world competence. Differences between simulated and real environments, known as the simulation-to-reality gap, can limit how well a trained system performs outside its training conditions.

36. How to Find More Models Beyond This Catalogue

Even this broad catalogue cannot list every existing model, source-code repository, academic implementation or commercial package. Many projects are published under the name of a research paper rather than under the name of a simulation platform. Others are stored in supplementary files, university repositories, project websites or less discoverable code-hosting services.

Search by phenomenon, not just by software

Searches are more effective when they combine the subject with terms describing the method or implementation. For example:

  • protein folding simulation GitHub
  • agent-based migration model source code
  • urban land-use simulation academic model
  • economic agent-based model repository
  • open-source groundwater simulation model
  • neural network electrophysiology model database
  • factory discrete-event simulation Python
  • climate model source code university
  • digital twin simulation framework GitHub
  • numerical model supplementary source code research paper

Search for individual academic models

Research-paper titles, author names, DOI records and supplementary materials can lead to implementations not easily discovered through general software searches. Useful places to look include institutional repositories, journal supplements, Zenodo, Figshare, OSF and discipline-specific archives.

Record each discovery systematically

A comprehensive catalogue should record more than the model's name. For each entry, useful fields include:

  • Model name, alternate names and scientific domain.
  • Phenomenon or system being modelled.
  • Mathematical method, assumptions and level of detail.
  • Software platform, programming language and dependencies.
  • Operating system, hardware requirements and whether a GPU or cluster is necessary.
  • Source-code location, official project page, documentation and published research.
  • Licence, access conditions, maintenance status and release date.
  • Validation evidence, benchmark results and known limitations.
  • Whether the entry is an individual model, model family, modelling framework or complete simulation application.

This information makes it possible to distinguish between a ready-to-run model, an engine that requires a model to be created, a demonstration example and a research code that depends on unavailable data or specialist software.

37. Validation, Reproducibility and the Limits of Simulation

A simulation is a representation of a system, not the system itself. A technically sophisticated program can still produce misleading results when its assumptions are unrealistic, its parameters are poorly estimated, its numerical method is unsuitable or its output has not been compared with observations.

Before relying on a model, it is important to determine what it represents, what it leaves out, how its parameters were chosen, whether its results have been validated against experimental or observational evidence, and whether independent researchers can reproduce its results.

Open source is valuable because it can make algorithms, assumptions and implementation details inspectable. However, publicly available code does not guarantee scientific validity, good documentation, reproducibility or active maintenance. Commercial software can also be rigorous, but its implementation may not be fully inspectable.

Simulation can reveal possible mechanisms, compare scenarios, estimate risks and test hypotheses that would otherwise be difficult to examine. It does not eliminate uncertainty, and a result should not be treated as a reliable forecast merely because it was generated by a computer.

Conclusion: A Universe of Models, Not Just Supercomputers

Computer simulation spans nearly every field of human inquiry. The same general idea—representing a system computationally and examining its behaviour—underlies a spreadsheet model of investment risk, a small agent-based population model, a medical physiology simulator, an industrial process model, a traffic network, a climate model and a cosmological simulation.

The distinction between ordinary desktop simulation and high-performance computing is primarily one of scale, complexity, resolution and computational demand. Many valuable models can be explored or developed on everyday hardware. Other models require specialised accelerators, clusters or supercomputers, but these are only one part of the wider simulation ecosystem.

Finding all simulation models is an ongoing discovery task rather than a finite enumeration. Specialist repositories, source-code archives, academic papers, software directories and open development platforms each expose different parts of the landscape. Combining them produces a substantially broader view than looking only at high-performance scientific software.

The long-term objective should be a searchable, continuously updated master index linking each model to its purpose, source code, documentation, discipline, computational requirements, licence and evidence of validation. Such an index would help researchers, engineers, students and independent developers locate relevant simulations—from the simplest models running in a browser to the most complex simulations of physical and biological reality.

  

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