Awesome Open Source

Programming Languages

Search results for automatic differentiation scientific machine learning

automatic-differentiation x

scientific-machine-learning x

7 search results found

Optimization.jl ⭐ 625

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.

Surrogates.jl ⭐ 302

Surrogate modeling and optimization for scientific machine learning (SciML)

Neuraloperators.jl ⭐ 206

DeepONets, (Fourier) Neural Operators, Physics-Informed Neural Operators, and more in Julia

Integrals.jl ⭐ 191

A common interface for quadrature and numerical integration for the SciML scientific machine learning organization

Nbodysimulator.jl ⭐ 122

A differentiable simulator for scientific machine learning (SciML) with N-body problems, including astrophysical and molecular dynamics

Multiscalearrays.jl ⭐ 69

A framework for developing multi-scale arrays for use in scientific machine learning (SciML) simulations

A Python library for training neural ODEs.

Related Searches

Scientific Machine Learning Sciml (84)

Julia Scientific Machine Learning (77)

Differential Equations Scientific Machine Learning (69)

Julia Automatic Differentiation (60)

Optimization Automatic Differentiation (40)

1-7 of 7 search results

Privacy | About | Terms | Follow Us On Twitter

Copyright 2018-2024 Awesome Open Source. All rights reserved.