Som Dhulipala's repositories
hamiltonian-nn
Code for our paper "Hamiltonian Neural Networks"
adnuts
An R package for NUTS sampling using ADMB
arviz
Exploratory analysis of Bayesian models with Python
awesome-neural-ode
A collection of resources regarding the interplay between differential equations, deep learning, dynamical systems, control and numerical methods.
BIhNNs
The code enables to perform Bayesian inference in an efficient manner through the use of Hamiltonian Neural Networks (HNNs) used with state-of-the-art sampling schemes like Hamiltonian Monte Carlo (HMC) and the No-U-Turn-Sampler (NUTS).
blackbear
BlackBear is a MOOSE-based code for simulating degradation processes in concrete and other structural materials.
deeponet
Learning nonlinear operators via DeepONet
deepxde
A library for scientific machine learning and physics-informed learning
diffusion
Denoising Diffusion Probabilistic Models
falcon
Fracturing And Liquid CONservation
gnn-powerflow
Graph Neural Network application in predicting AC Power Flow calculation. Developed with Pytorch Geometric framework. My Master Thesis at Eindhoven University of Technology
IGAPack-PhaseField
Second and fourth-order adaptive phase field modeling of fracture using PHT-splines in the framework of IGA.
large_media
A repository for storing large images and movies associated with MOOSE documentation.
lightning
Build and train PyTorch models and connect them to the ML lifecycle using Lightning App templates, without handling DIY infrastructure, cost management, scaling, and other headaches.
malamute
Advanced manufacturing modeling and simulation
modulus
Open-source deep-learning framework for building, training, and fine-tuning deep learning models using state-of-the-art Physics-ML methods
moose
Multiphysics Object Oriented Simulation Environment
neml
Modular consitutive modeling library for structural materials
NUTS
python version of the No-U-Turn Sampler (NUTS) from Hoffman & Gelman, 2011
sfepy
Main SfePy repository
somu15.github.io
Source code for website
torchdiffeq
Differentiable ODE solvers with full GPU support and O(1)-memory backpropagation.