Physics-informed neural networks for Windkessel parameter estimation from MRI images

Resources for "The Craft of Finite Difference Computing with Partial Differential Equations" by H. P. Langtangen

Official implementation code of the paper: "GCN-FFNN: A Two-Stream Deep Model for Learning Solution to Partial Differential Equations".

Deep-learning based time stepping for spatio-temporal processes

Deep Convolutional Ritz Method

A series of Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep Learning in Python using Scikit-Learn, Keras and TensorFlow 2.

PhyGeoNet: Physics-Informed Geometry-Adaptive Convolutional Neural Networks for Solving Parametric PDEs on Irregular Domain

Physics Informed Deep Learning: Data-driven Solutions and Discovery of Nonlinear Partial Differential Equations

Second and fourth-order adaptive phase field modeling of fracture using PHT-splines in the framework of IGA.

A library for scientific machine learning and physics-informed learning