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.
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)
Quadrature.jl: Common interface for quadrature and numerical integration
SparsityDetection.jl: Automated Jacobian and Hessian sparsity patterns
AutoOffload.jl: Automatic GPU, TPU, FPGA, Xeon Phi, Multithreaded, Distributed, etc. offloading
Please see the developer documentation for information on getting started with developing in the SciML organization. Please see Colprac for the community development practices.
MIT 18.337J/6.338J: Parallel Computing and Scientific Machine Learning
MIT 6.S083/18.S190: Computational thinking with Julia + application to the COVID-19 pandemic
MIT 18.S096 Special Subject in Mathematics: Applications of Scientific Machine Learning