Nonnegative Matrix Factorization + k-means clustering and physics constraints for Unsupervised and Physics-Informed Machine Learning
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Updated
May 26, 2024 - HTML
Nonnegative Matrix Factorization + k-means clustering and physics constraints for Unsupervised and Physics-Informed Machine Learning
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.
Fast and simple nonlinear solvers for the SciML common interface. Newton, Broyden, Bisection, Falsi, and more rootfinders on a standard interface.
High-performance and differentiation-enabled nonlinear solvers (Newton methods), bracketed rootfinding (bisection, Falsi), with sparsity and Newton-Krylov support.
Chemical reaction network and systems biology interface for scientific machine learning (SciML). High performance, GPU-parallelized, and O(1) solvers in open source software.
The Base interface of the SciML ecosystem
Python Suite for Advanced General Ensemble Simulations
The lightweight Base library for shared types and functionality for defining differential equation and scientific machine learning (SciML) problems
LinearSolve.jl: High-Performance Unified Interface for Linear Solvers in Julia. Easily switch between factorization and Krylov methods, add preconditioners, and all in one interface.
Tools for easily handling objects like arrays of arrays and deeper nestings in scientific machine learning (SciML) and other applications
Build and simulate jump equations like Gillespie simulations and jump diffusions with constant and state-dependent rates and mix with differential equations and scientific machine learning (SciML)
GPU-acceleration routines for DifferentialEquations.jl and the broader SciML scientific machine learning ecosystem
Designs for new Base array interface primitives, used widely through scientific machine learning (SciML) and other organizations
A library of noise processes for stochastic systems like stochastic differential equations (SDEs) and other systems that are present in scientific machine learning (SciML)
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
Reservoir computing utilities for scientific machine learning (SciML)
The Rheoinformatic lab website
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 ranked list of awesome atomistic machine learning projects ⚛️🧬💎.
Operator Inference for data-driven, non-intrusive model reduction of dynamical systems.
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