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A simulator for ecological dynamics written in Julia.
Interpolation and function approximation with JAX
Oryx is a library for probabilistic programming and deep learning built on top of Jax.
A library for solving differential equations using neural networks based on PyTorch, used by multiple research groups around the world, including at Harvard IACS.
DeepONets, (Fourier) Neural Operators, Physics-Informed Neural Operators, and more in Julia
DeepONets, (Fourier) Neural Operators, Physics-Informed Neural Operators, and more in Julia
A JAX-based research framework for writing differentiable numerical simulators with arbitrary discretizations
An open large-scale dataset for training high-resolution physics emulators in hybrid multi-scale climate simulators.
Transformer creation from scratch using Jax.
Trajectory simulations for point particles in the Ocean & Atmosphere
Software design principles for machine learning applications
🌊 Julia software for fast, friendly, flexible, ocean-flavored fluid dynamics on CPUs and GPUs
Home for "How To Scale Your Model", a short blog-style textbook about scaling LLMs on TPUs
Temporal Convolutional Neural Network for the Classification of Satellite Image Time Series
A pure-functional implementation of a machine learning transformer model in Python/JAX
An implementation of chunked, compressed, N-dimensional arrays for Python.
Optimize Julia Functions With MLIR and XLA for High-Performance Execution on CPU, GPU, TPU and more.
A minimal JAX library for connectivity analysis at scales
A 15TB Collection of Physics Simulation Datasets
Graph Neural Networks in Julia
[Neurips 2024] A benchmark suite for autoregressive neural emulation of PDEs. (≥46 PDEs in 1D, 2D, 3D; Differentiable Physics; Unrolled Training; Rollout Metrics)
Scientific Inkscape: Inkscape extensions for figure resizing and editing
R package for working with spatial absorbing Markov chains
Differentiable, Hardware Accelerated, Molecular Dynamics