Python package for high-performance spatial light modulator (SLM) control and holography. Supports features from aberration-corrected 3D point clouds to automated Fourier-domain calibrations.

SXDM is a library for analyzing Scanning X-Ray Diffraction Microscopy/X-Ray Fluorescence (XRF) data from 26-ID-C (APS) Advanced Photon Source. The major focus is on Scanning Microscopy frames colle…

Use commands in English to control Blender with OpenAI's GPT-4

ISL lab website

Algorithms for Decision Making textbook

PiSCAT is a python-based package with a graphical user interface for performing high-performance analysis on a variety of iSCAT measurements.

Physics-informed variational autoencoder for LED array microscopy.

DeepXDE and PINN

Physics Informed Machine Learning Tutorials (Pytorch and Jax)

Codes for the paper "Super-resolved Virtual Staining of Label-free Tissue Using Diffusion Models"

This repository is the official project page of the course AI in the Sciences and Engineering, ETH Zurich.

Learning nonlinear operators via DeepONet based on the universal approximation theorem of operators

Learning in infinite dimension with neural operators.

A PyTorch library entirely dedicated to neural differential equations, implicit models and related numerical methods

A collection of resources regarding the interplay between differential equations, deep learning, dynamical systems, control and numerical methods.

A curated list of awesome TikZ documentations, libraries and resources

Differentiable wave optics simulation library built on PyTorch

A PyTorch-based library for working with 3D and 2D convolutional neural networks, with focus on semantic segmentation of volumetric biomedical image data

Unofficial implementation in Python porting of the book "Algorithms for Optimization" (2019) MIT Press by By Mykel J. Kochenderfer and Tim A. Wheeler

Jupyter notebooks associated with the Algorithms for Optimization textbook

My personal website

**Official** 李宏毅 (Hung-yi Lee) 機器學習 Machine Learning 2022 Spring

IPython notebooks with demo code intended as a companion to the book "Data-Driven Science and Engineering: Machine Learning, Dynamical Systems, and Control" by Steven L. Brunton and J. Nathan Kutz

Deep Reinforcement Learning

Course 18.S191 at MIT, Fall 2022 - Introduction to computational thinking with Julia

Code for A Programmer's Introduction to Mathematics

Software that can generate photos from paintings, turn horses into zebras, perform style transfer, and more.