The lightweight Base library for shared types and functionality for defining differential equation and scientific machine learning (SciML) problems
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Updated
May 25, 2024 - Julia
The lightweight Base library for shared types and functionality for defining differential equation and scientific machine learning (SciML) problems
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
Differentiable SDE solvers with GPU support and efficient sensitivity analysis.
Tools for Stochastic Simulation using diffusion models (R).
Hierarchical continuous time state space modelling
Pre-built implicit layer architectures with O(1) backprop, GPUs, and stiff+non-stiff DE solvers, demonstrating scientific machine learning (SciML) and physics-informed machine learning methods
📦 Python library for Stochastic Processes Simulation and Visualisation
Easy scientific machine learning (SciML) parameter estimation with pre-built loss functions
A Python library for simulating multilayer magnetic structures.
A simulation framework for nonequilibrium statistical physics
Geometric Numerical Integration in Julia
A Julia package for critical transitions in dynamical systems with time-dependent forcing
A library of premade problems for examples and testing differential equation solvers and other SciML scientific machine learning tools
Multi-language suite for high-performance solvers of differential equations and scientific machine learning (SciML) components. Ordinary differential equations (ODEs), stochastic differential equations (SDEs), delay differential equations (DDEs), differential-algebraic equations (DAEs), and more in Julia.
Differential equation problem specifications and scientific machine learning for common financial models
Solvers for stochastic differential equations which connect with the scientific machine learning (SciML) ecosystem
Extension functionality which uses Stan.jl, DynamicHMC.jl, and Turing.jl to estimate the parameters to differential equations and perform Bayesian probabilistic scientific machine learning
Tutorials for doing scientific machine learning (SciML) and high-performance differential equation solving with open source software.
Parabolic PDE resolution with Tensor Networks using Backward-Forward Stochastic Differential Equations
Numerical schemes to find solutions of SDEs
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