Scientific machine learning (SciML) benchmarks, AI for science, and (differential) equation solvers. Covers Julia, Python (PyTorch, Jax), MATLAB, R
MIT License
Multi-language suite for high-performance solvers of differential equations and scientific machin...
Provides a platform for the Julia community to compare AI models' abilities in generating syntact...
High performance ordinary differential equation (ODE) and differential-algebraic equation (DAE) s...
🌊 Julia software for fast, friendly, flexible, ocean-flavored fluid dynamics on CPUs and GPUs
Cross-architecture parallel algorithms for Julia's GPU backends, from a unified KernelAbstraction...
Unitary and Lindbladian evolution in Julia
Implementations of Infinitesimal Continuous Normalizing Flows Algorithms in Julia
A benchmarking framework for the Julia language
An acausal modeling framework for automatically parallelized scientific machine learning (SciML) ...
Performance and data profiles
A common interface for quadrature and numerical integration for the SciML scientific machine lear...
Robust, Fast, and Parallel Global Sensitivity Analysis (GSA) in Julia
Wrappers for the SciPy differential equation solvers for the SciML Scientific Machine Learning or...
Tutorials for doing scientific machine learning (SciML) and high-performance differential equatio...
Mathematical Optimization in Julia. Local, global, gradient-based and derivative-free. Linear, Qu...