KirillZubov
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Tensors and Dynamic neural networks in Python with strong GPU acceleration
Materials for the Learn PyTorch for Deep Learning: Zero to Mastery course.
Large-Scale Multimodal Dataset of Astronomical Data
A 15TB Collection of Physics Simulation Datasets
Projects and exercises for the latest Deep Learning ND program https://www.udacity.com/course/deep-learning-nanodegree--nd101
Welcome to the Physics-based Deep Learning Book (v0.2)
Efficient and type-stable physical quantities in Julia
Code for "Discovering Symbolic Models from Deep Learning with Inductive Biases"
DeepONet & FNO (with practical extensions)
Code for paper "Multiple Physics Pretraining for Physical Surrogate Models
High-Performance Symbolic Regression in Python and Julia
Distributed High-Performance Symbolic Regression in Julia
Learning in infinite dimension with neural operators.
High-performance and differentiation-enabled nonlinear solvers (Newton methods), bracketed rootfinding (bisection, Falsi), with sparsity and Newton-Krylov support.
Pytorch-based framework for solving parametric constrained optimization problems, physics-informed system identification, and parametric model predictive control.
Adaptive FNO transformer - official Pytorch implementation
Julia code for the book Reinforcement Learning An Introduction
Implementation of the paper "Self-Adaptive Physics-Informed Neural Networks using a Soft Attention Mechanism" [AAAI-MLPS 2021]
A library of data interpolation and smoothing functions
Play atmospheric modelling like it's LEGO.
The Deep Learning with Julia book, using Flux.jl.
Dataset preprocessor for the KGS go dataset, eg according to Clark and Storkey input planes
A PyTorch-based framework for Quantum Classical Simulation, Quantum Machine Learning, Quantum Neural Networks, Parameterized Quantum Circuits with support for easy deployments on real quantum compu…
Research package for automatic differentiation of programs containing discrete randomness.
Материалы курса Deep Learning на пальцах
Library for the numerical simulation of closed as well as open quantum systems.