Chemical reaction network and systems biology interface for scientific machine learning (SciML). High performance, GPU-parallelized, and O(1) solvers in open source software.
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Updated
May 24, 2024 - Julia
Chemical reaction network and systems biology interface for scientific machine learning (SciML). High performance, GPU-parallelized, and O(1) solvers in open source software.
High performance ordinary differential equation (ODE) and differential-algebraic equation (DAE) solvers, including neural ordinary differential equations (neural ODEs) and scientific machine learning (SciML)
Designs for new Base array interface primitives, used widely through scientific machine learning (SciML) and other organizations
Fast and flexible glacier ice flow models
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
Global documentation for the Julia SciML Scientific Machine Learning Organization
Winning Submission for ice segmentation, using image processing. Organized by British Antarctic Survey(BAS) And SciMl University of Leeds
High-performance and differentiation-enabled nonlinear solvers (Newton methods), bracketed rootfinding (bisection, Falsi), with sparsity and Newton-Krylov support.
The lightweight Base library for shared types and functionality for defining differential equation and scientific machine learning (SciML) problems
The Base interface of the SciML ecosystem
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.
Julia implementation of the Earth4All model using the WorldDynamics framework.
Optimal experimental design of ODE and DAE systems in julia
Tools for easily handling objects like arrays of arrays and deeper nestings in scientific machine learning (SciML) and other applications
The SciML Scientific Machine Learning Software Organization Website
A component of the DiffEq ecosystem for enabling sensitivity analysis for scientific machine learning (SciML). Optimize-then-discretize, discretize-then-optimize, adjoint methods, and more for ODEs, SDEs, DDEs, DAEs, etc.
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.
Fast and simple nonlinear solvers for the SciML common interface. Newton, Broyden, Bisection, Falsi, and more rootfinders on a standard interface.
Scientific machine learning (SciML) benchmarks, AI for science, and (differential) equation solvers. Covers Julia, Python (PyTorch, Jax), MATLAB, R
Solving Universal Differential Equations in Julia
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