SciML

Documentation and Tutorials

The SciML organization is an opinionated collection of tools for scientific machine learning and differential equation modeling. The organization provides well-maintained tools which compose together as a coherent ecosystem. The following are the relevant resources for users interested in the functionality.

Differential Equations

These resources cover:

  • Discrete equations (function maps, discrete stochastic (Gillespie/Markov) simulations)
  • Ordinary differential equations (ODEs)
  • Split and Partitioned ODEs (Symplectic integrators, IMEX Methods)
  • Stochastic ordinary differential equations (SODEs or SDEs)
  • Random differential equations (RODEs or RDEs)
  • Differential algebraic equations (DAEs)
  • Delay differential equations (DDEs)
  • Mixed discrete and continuous equations (Hybrid Equations, Jump Diffusions)
  • (Stochastic) partial differential equations ((S)PDEs) (with both finite difference and finite element methods)

Partial Differential Equation Modeling

Scientific Machine Learning Model Discovery

Surrogate Acceleration and Optimization

Modeling Languages and Domain-Specific Languages

Modeling Tools and Primatives

Numerical Tools and Primatives

Developer Documentation

Please see the developer documentation for information on getting started with developing in the SciML organization. Please see Colprac for the community development practices.

External Tutorials and Teaching Materials

External Applications Libraries

There are many external libraries which connect and utilize SciML utilities under the hood. The following an incomplete list of software organizations providing domain modeling tools that are built upon SciML. If you would like your institution’s tools added to the list, please open a pull request.