SciML / SciMLBenchmarksOutputLinks
SciML-Bench Benchmarks for Scientific Machine Learning (SciML), Physics-Informed Machine Learning (PIML), and Scientific AI Performance
☆24Updated this week
Alternatives and similar repositories for SciMLBenchmarksOutput
Users that are interested in SciMLBenchmarksOutput are comparing it to the libraries listed below
Sorting:
- Efficient, Accurate, and Streamlined Training of Physics-Informed Neural Networks☆58Updated 5 months ago
- No need to train, he's a smooth operator☆45Updated last month
- Physics-Enhanced Regression for Initial Value Problems☆20Updated last year
- Common types and interface for discretizers of ModelingToolkit PDESystems.☆13Updated last month
- Automates steady and unsteady adjoints (general solvers and ODEs respectively). Forward and reverse mode algorithmic differentiation arou…☆29Updated last month
- A package for multi-dimensional integration using monte carlo methods☆40Updated last year
- Training materials for ModelingToolkit and JuliaSim☆38Updated 2 years ago
- Machine learning from scratch in Julia☆32Updated 6 months ago
- High Oscillatory Ordinary Differential Equation Solver in Julia☆17Updated 9 months ago
- Julia package for hierarchical matrices☆28Updated 10 months ago
- Plot your Ferrite.jl data☆33Updated 5 months ago
- Tutorials for doing scientific machine learning (SciML) and high-performance differential equation solving with open source software.☆22Updated last year
- ☆19Updated 9 months ago
- Differentiable matrix factorizations using ImplicitDifferentiation.jl.☆30Updated 2 years ago
- ☆36Updated 2 years ago
- A library of systems of partial differential equations, as defined with ModelingToolkit.jl in Julia☆28Updated last month
- Checkpointing for Automatic Differentiation☆56Updated this week
- High-level model-order reduction to automate the acceleration of large-scale simulations☆40Updated last week
- ☆19Updated last year
- A multigrid package in Julia: smoothed aggregation AMG + geometric multigrid.☆18Updated last year
- Implicit Layer Machine Learning via Deep Equilibrium Networks, O(1) backpropagation with accelerated convergence.☆57Updated last month
- Workshop materials for training in scientific computing and scientific machine learning☆39Updated last month
- Taylor-mode automatic differentiation for higher-order derivatives☆80Updated last month
- Structure Preserving Machine Learning Models in Julia☆50Updated 3 weeks ago
- ☆20Updated 3 months ago
- Code for paper https://arxiv.org/abs/2306.07961☆53Updated last year
- ☆31Updated 2 years ago
- A Julia package to handle spherical harmonic functions☆30Updated 5 months ago
- DeepONets, (Fourier) Neural Operators, Physics-Informed Neural Operators, and more in Julia☆25Updated last week
- Fast uncertainty quantification for scientific machine learning (SciML) and differential equations☆69Updated last week