Fast and differentiable implementations of matrix exponentials, Krylov exponential matrix-vector multiplications ("expmv"), KIOPS, ExpoKit functions, and more. All your exponential needs in SciML form.
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Updated
Jun 3, 2024 - Julia
Fast and differentiable implementations of matrix exponentials, Krylov exponential matrix-vector multiplications ("expmv"), KIOPS, ExpoKit functions, and more. All your exponential needs in SciML form.
The lightweight Base library for shared types and functionality for defining differential equation and scientific machine learning (SciML) problems
The Rheoinformatic lab website
Chemical reaction network and systems biology interface for scientific machine learning (SciML). High performance, GPU-parallelized, and O(1) solvers in open source software.
Mathematical Optimization in Julia. Local, global, gradient-based and derivative-free. Linear, Quadratic, Convex, Mixed-Integer, and Nonlinear Optimization in one simple, fast, and differentiable interface.
Nonnegative Matrix Factorization + k-means clustering and physics constraints for Unsupervised and Physics-Informed Machine Learning
High-performance and differentiation-enabled nonlinear solvers (Newton methods), bracketed rootfinding (bisection, Falsi), with sparsity and Newton-Krylov support.
Solve Fractional Differential Equations using high performance numerical methods
High performance ordinary differential equation (ODE) and differential-algebraic equation (DAE) solvers, including neural ordinary differential equations (neural ODEs) and scientific machine learning (SciML)
A general interface for symbolic indexing of SciML objects used in conjunction with Domain-Specific Languages
Tools for easily handling objects like arrays of arrays and deeper nestings in scientific machine learning (SciML) and other applications
A library for scientific machine learning and physics-informed learning
The SciML Scientific Machine Learning Software Organization Website
🏆 A ranked list of awesome atomistic machine learning projects ⚛️🧬💎.
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
Probabilistic Programming and Nested sampling in JAX
FastVPINNs - A tensor-driven acceleration of VPINNs for complex geometries
Robust, Fast, and Parallel Global Sensitivity Analysis (GSA) in Julia
Code for the paper "Poseidon: Efficient Foundation Models for PDEs"
The Base interface of the SciML ecosystem
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