Surrogate modeling and optimization for scientific machine learning (SciML)
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Updated
Jul 8, 2024 - Julia
Surrogate modeling and optimization for scientific machine learning (SciML)
High-performance and differentiation-enabled nonlinear solvers (Newton methods), bracketed rootfinding (bisection, Falsi), with sparsity and Newton-Krylov support.
Side-channel toolkit in Julia
Automatic Finite Difference PDE solving with Julia SciML
Tools for building non-allocating pre-cached functions in Julia, allowing for GC-free usage of automatic differentiation in complex codes
Affine Invariant Markov Chain Monte Carlo (MCMC) Ensemble sampler
Distributed Julia arrays using the MPI protocol
Inference of microbial interaction networks from large-scale heterogeneous abundance data
Fast Fourier transforms of MPI-distributed Julia arrays
Geostatistical Inversion
Readily pin Julia threads to CPU processors
Fast Poisson Random Numbers in pure Julia for scientific machine learning (SciML)
Automatic optimization and parallelization for Scientific Machine Learning (SciML)
NODAL is an Open Distributed Autotuning Library in Julia
Measuring memory bandwidth using TheBandwidthBenchmark
Bayesian Information Gap Decision Theory
Robust pmap calls for efficient parallelization and high-performance computing
Meta Programming Tools
Automated storage and retrieval of results for functions calls
An array type for MPI halo data exchange in Julia
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