ahenkes1

cubenet

3D Roto-translation equivariant convolutions

HENKES_GAN

Code of the publication "Three-dimensional microstructure generation using generative adversarial neural networks in the context of continuum micromechanics" published in https://doi.org/10.1016/j.cma.2022.115497 by Alexander Henkes and Henning Wessels from TU Braunschweig.

HENKES_PINN

Code of the publication "Physics informed neural networks for continuum micromechanics" published in https://doi.org/10.1016/j.cma.2022.114790 by Alexander Henkes and Henning Wessels from TU Braunschweig and Rolf Mahnken from University of Paderborn.

HENKES_SNN

Code of the publication "Spiking neural networks for nonlinear regression" published in https://doi.org/10.1098/rsos.231606 by Alexander Henkes from ETH Zürich, Jason K. Eshraghian from University of California, Santa Cruz and Henning Wessels from TU Braunschweig.

meshGraphNets_pytorch

PyTorch implementations of Learning Mesh-based Simulation With Graph Networks

Physics-informed-Neural-Network-with-Graph-Embedding

pydec

PyDEC: A Python Library for Discretizations of Exterior Calculus for simplicial complexes of any dimension embedded or not and for cubical complexes of any dimension. Implements discrete exterior derivative as coboundary, a Delaunay Hodge star operator and lowest order Whitney forms.

snntorch

Deep and online learning with spiking neural networks in Python

snntorch

Deep and online learning with spiking neural networks in Python

lava-dl

Deep Learning library for Lava

ahenkes1/HENKES_PINN

Code of the publication "Physics informed neural networks for continuum micromechanics" published in https://doi.org/10.1016/j.cma.2022.114790 by Alexander Henkes and Henning Wessels from TU Braunschweig and Rolf Mahnken from University of Paderborn.

ahenkes1/HENKES_GAN

Code of the publication "Three-dimensional microstructure generation using generative adversarial neural networks in the context of continuum micromechanics" published in https://doi.org/10.1016/j.cma.2022.115497 by Alexander Henkes and Henning Wessels from TU Braunschweig.

ahenkes1/HENKES_SNN

Code of the publication "Spiking neural networks for nonlinear regression" published in https://doi.org/10.1098/rsos.231606 by Alexander Henkes from ETH Zürich, Jason K. Eshraghian from University of California, Santa Cruz and Henning Wessels from TU Braunschweig.

ahenkes1/snntorch

Deep and online learning with spiking neural networks in Python

ahenkes1/meshGraphNets_pytorch

PyTorch implementations of Learning Mesh-based Simulation With Graph Networks

ahenkes1/Physics-informed-Neural-Network-with-Graph-Embedding

ahenkes1/pydec

PyDEC: A Python Library for Discretizations of Exterior Calculus for simplicial complexes of any dimension embedded or not and for cubical complexes of any dimension. Implements discrete exterior derivative as coboundary, a Delaunay Hodge star operator and lowest order Whitney forms.

ahenkes1/cubenet

3D Roto-translation equivariant convolutions

ahenkes1/hamiltorch

PyTorch-based library for Riemannian Manifold Hamiltonian Monte Carlo (RMHMC) and inference in Bayesian neural networks

ahenkes1/IEBCS

ICNS Event Based Camera Simulator

ahenkes1/jaxpi

ahenkes1/NUTS

python version of the No-U-Turn Sampler (NUTS) from Hoffman & Gelman, 2011

ahenkes1/tuning_playbook

A playbook for systematically maximizing the performance of deep learning models.

ahenkes1/geomstats

Computations and statistics on manifolds with geometric structures.

ahenkes1/lava

A Software Framework for Neuromorphic Computing

ahenkes1/lava-dl

Deep Learning library for Lava

ahenkes1/modulus-sym

Framework providing pythonic APIs, algorithms and utilities to be used with Modulus core to physics inform model training as well as higher level abstraction for domain experts

ahenkes1/MParT

Monotone Parameterization Toolkit (MParT): A core library for constructing and using transport maps.

ahenkes1/OT-ICNN

Learning the optimal transport map via input convex neural neworks

ahenkes1/pytorch-image-models

PyTorch image models, scripts, pretrained weights -- ResNet, ResNeXT, EfficientNet, NFNet, Vision Transformer (ViT), MobileNet-V3/V2, RegNet, DPN, CSPNet, Swin Transformer, MaxViT, CoAtNet, ConvNeXt, and more

ahenkes1/pytorch-minimize

Newton and Quasi-Newton optimization with PyTorch

ahenkes1/sinabs

A deep learning library for spiking neural networks which is based on PyTorch, focuses on fast training and supports inference on neuromorphic hardware.

ahenkes1/tch-rs

Rust bindings for the C++ api of PyTorch.

ahenkes1/v2e

V2E: From video frames to DVS events

ahenkes1/wpinns