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HKUST Thesis LaTeX3 Template (Available on Overleaf/TeXPage)
Python-based research interface for blackbox and hyperparameter optimization, based on the internal Google Vizier Service.
Latex code for making neural networks diagrams
Solver-in-the-Loop: Learning from Differentiable Physics to Interact with Iterative PDE-Solvers
Physics-Driven Deep Learning for PDEs and Inverse Problems
A deep learning based method for solving high dimensional partial differential equations based on its weak form
Group project for Deep Learning: Algorithms and Applications in Peking University 2018 Spring. This is a brief survey, discussion and implementation for deep Ritz method.
A beautiful, simple, clean, and responsive Jekyll theme for academics
Examples for https://github.com/optuna/optuna
An open source AutoML toolkit for automate machine learning lifecycle, including feature engineering, neural architecture search, model compression and hyper-parameter tuning.
Physics-informed neural networks with hard constraints for inverse design
FastVPINNs - A tensor-driven acceleration of VPINNs for complex geometries
Solve forward and inverse problems related to partial differential equations using finite basis physics-informed neural networks (FBPINNs)
A place to share problems solved with SciANN
Physics-guided neural network framework for elastic plates
Welcome to the Physics-based Deep Learning Book v0.3 - the GenAI Edition
A library for scientific machine learning and physics-informed learning
Python implementation of the Method of Moving Asymptotes (MMA)
Links to works on deep learning algorithms for physics problems, TUM-I15 and beyond
Differentiable Finite Element Method with JAX