SciML / SciMLBenchmarksOutput
SciML-Bench Benchmarks for Scientific Machine Learning (SciML), Physics-Informed Machine Learning (PIML), and Scientific AI Performance
☆19Updated last week
Alternatives and similar repositories for SciMLBenchmarksOutput:
Users that are interested in SciMLBenchmarksOutput are comparing it to the libraries listed below
- No need to train, he's a smooth operator☆43Updated 3 months ago
- Efficient, Accurate, and Streamlined Training of Physics-Informed Neural Networks☆57Updated 3 months ago
- Physics-Enhanced Regression for Initial Value Problems☆19Updated last year
- Differentiable matrix factorizations using ImplicitDifferentiation.jl.☆30Updated last year
- Code for paper https://arxiv.org/abs/2306.07961☆53Updated 8 months ago
- Common types and interface for discretizers of ModelingToolkit PDESystems.☆12Updated last month
- JuliaLab Website☆22Updated this week
- Checkpointing for Automatic Differentiation☆53Updated this week
- Implicit Layer Machine Learning via Deep Equilibrium Networks, O(1) backpropagation with accelerated convergence.☆51Updated 2 weeks ago
- A package for multi-dimensional integration using monte carlo methods☆39Updated last year
- Machine learning from scratch in Julia☆31Updated 2 months ago
- Proof of Concept: a C-callable GPU-enabled parallel 2-D heat diffusion solver written in Julia using CUDA, MPI and graphics☆24Updated 4 years ago
- Julia implementation of stochastic optimization algorithms for large-scale optimal transport.☆17Updated 3 years ago
- ☆28Updated 3 years ago
- A library of systems of partial differential equations, as defined with ModelingToolkit.jl in Julia☆28Updated 6 months ago
- lecture materials of the ML for Physics course 2021 in Perimeter Institute☆21Updated 3 years ago
- Workshop materials for training in scientific computing and scientific machine learning☆36Updated 9 months ago
- Limited-Memory Factorization of Symmetric Matrices☆21Updated 2 months ago
- Integrating Neural Ordinary Differential Equations, the Method of Lines, and Graph Neural Networks☆18Updated last year
- Tutorials for doing scientific machine learning (SciML) and high-performance differential equation solving with open source software.☆20Updated last year
- Julia package for hierarchical matrices☆28Updated 3 months ago
- Taylor-mode automatic differentiation for higher-order derivatives☆76Updated last week
- Autosuggestions for function keywords☆20Updated 8 months ago
- ☆13Updated 2 months ago
- Data structures for graph neural network☆18Updated 8 months ago
- Comparsion of Julia's GPU Kernel based ODE solvers with other open-source GPU ODE solvers☆24Updated last year
- High Oscillatory Ordinary Differential Equation Solver in Julia☆17Updated 2 months ago
- Automatic differentiation of FEniCS and Firedrake models in Julia☆13Updated 3 years ago
- A Julia package to handle spherical harmonic functions☆30Updated 2 months ago
- ☆36Updated last year