Phase field model for material science applications.

GP based symbolic regression framework accelerated by CUDA

Application of Graph Neural Networks to predict material properties from their microstructures.

Tool to plot, manipulate and deconvolute FTIR spectra.

Official implementation of CoNSAL for analytical Lyapunov function discovery

High-performance multi-material continuum physics engine in Taichi

PRISMS-PF: An Open-Source Phase-Field Modeling Framework

Deep learning for molecules and materials book

ExaDiS (Exascale Dislocation Simulator) is a portable library for Discrete Dislocation Dynamics simulations that runs on GPU

Modelling the behaviour of dislocation loops and defect in tungsten

Python Laboratory for Dislocation Dynamics

TORAX: Tokamak transport simulation in JAX

Modern toolbox for impurity transport, neutrals and radiation modeling in magnetically-confined plasmas

Code for Section 2 of the paper "Data-driven exploration and continuum modeling of dislocation networks".

Python implementation of the BACON algorithms for automated scientific discovery

Initial Commit

A Parallel SAT Solver with GPU Accelerated Inprocessing

ParaDiS (Parallel Dislocation Simulator) is a massively parallel Discrete Dislocation Dynamics simulation tool to simulate motion and interaction of large ensembles of dislocations

Turning SymPy expressions into PyTorch modules.

[T-ITS] Driving Behavior Modeling using Naturalistic Human Driving Data with Inverse Reinforcement Learning

Inverse Reinforcement Learning via State Marginal Matching, CoRL 2020

Implementations of selected inverse reinforcement learning algorithms.

Code repository for the paper "Maximum likelihood constraint inference on continuous state spaces"

Deep Reinforcement Learning algorithms implemented in PyTorch

In this project, we use the maximum entropy principle in Inverse reinforcement learning to learn soft constraints from demonstrations obtained from an agent interacting with a non-deterministic MDP…

An AI program that plays Flappy Bird using reinforcement learning.