SciML: Differentiable Modeling and Simulation Combined with Machine Learning
The SciML organization is a collection of tools for solving equations and modeling systems
developed in the Julia programming language with bindings to other languages such as R and
Python. The organization provides well-maintained tools which compose together as a
coherent ecosystem. It has a coherent development principle, unified APIs over large
collections of equation solvers, pervasive differentiability and sensitivity analysis, and
features many of the highest performance and parallel implementations one can find.
An acausal modeling framework for automatically parallelized scientific machine learning (SciML) in Julia. A computer algebra system for integrated symbolics for physics-informed machine learning a…
An acausal modeling framework for automatically parallelized scientific machine learning (SciML) in Julia. A computer algebra system for integrated symbolics for physics-informed machine learning and automated transformations of differential equations
A Julia package for Deep Backwards Stochastic Differential Equation (Deep BSDE) and Feynman-Kac methods to solve high-dimensional PDEs without the curse of dimensionality
SciML/HighDimPDE.jl's past year of commit activity
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