Damien Sicard's repositories
altar31
Config files for my GitHub profile.
basalt
A Machine Learning framework from scratch in Pure Mojo 🔥
burn
Burn is a new comprehensive dynamic Deep Learning Framework built using Rust with extreme flexibility, compute efficiency and portability as its primary goals.
continuiti
Learning function operators with neural networks.
dash
Data Apps & Dashboards for Python. No JavaScript Required.
deeponet-jax-bench
Benchmarking DeepONet with JAX
deepxde
A library for scientific machine learning and physics-informed learning
Flux.jl
Relax! Flux is the ML library that doesn't make you tensor
Gridap.jl
Grid-based approximation of partial differential equations in Julia
Makie.jl
Interactive data visualizations and plotting in Julia
PackageCompiler.jl
Compile your Julia Package
polars
Dataframes powered by a multithreaded, vectorized query engine, written in Rust
pytorch
Tensors and Dynamic neural networks in Python with strong GPU acceleration
jax
Composable transformations of Python+NumPy programs: differentiate, vectorize, JIT to GPU/TPU, and more
machine_learning_refined
Notes, examples, and Python demos for the 2nd edition of the textbook "Machine Learning Refined" (published by Cambridge University Press).
modulus
Open-source deep-learning framework for building, training, and fine-tuning deep learning models using state-of-the-art Physics-ML methods
mojo
The Mojo Programming Language
neuraloperator
Learning in infinite dimension with neural operators.
PINA
Physics-Informed Neural networks for Advanced modeling
pytorch-widedeep
A flexible package for multimodal-deep-learning to combine tabular data with text and images using Wide and Deep models in Pytorch
ResUNet-DeepONet-Plasticity
Implementation of a ResUNet-based DeepONet for predicting stress distribution on variable input geometries subject to variable loads. A ResUNet is used in the trunk network to encode the variable input geometries, and a feed-forward neural network is used in the branch to encode the loading parameters.
S-DeepONet
A sequential DeepONet model implementation that uses a recurrent neural network (GRU and LSTM) in the branch and a feed-forward neural network in the trunk. The branch network efficiently encodes time-dependent input functions, and the trunk network captures the spatial dependence of the full-field data.