Publication

Learning Dynamics of Spring-Mass Models with Physics-Informed Graph Neural Networks

Related publications (33)

About
Privacy
Disclaimer

Graph Chatbot

Chat with Graph Search

Ask any question about EPFL courses, lectures, exercises, research, news, etc. or try the example questions below.

DISCLAIMER: The Graph Chatbot is not programmed to provide explicit or categorical answers to your questions. Rather, it transforms your questions into API requests that are distributed across the various IT services officially administered by EPFL. Its purpose is solely to collect and recommend relevant references to content that you can explore to help you answer your questions.

Learning Dynamics of Spring-Mass Models with Physics-Informed Graph Neural Networks

Graph Chatbot

Chat with Graph Search

PDE-Aware Deep Learning for Inverse Problems in Cardiac Electrophysiology

Recursive Input And State Estimation: A General Framework For Learning From Time Series With Missing Data

Physics-enhanced machine learning with symmetry-adapted and long-range representations

Teaching optics to a machine learning network

Inverse Modelling and Predictive Inference in Continuum Mechanics: a Data-Driven Approach

Controlling oscillations in high-order Discontinuous Galerkin schemes using artificial viscosity tuned by neural networks

MATHICSE Technical Report: Controlling oscillations in high-order Discontinuous Galerkin schemes using artificial\ viscosity tuned by neural networks

Detecting troubled-cells on two-dimensional unstructured grids using a neural network

An artificial neural network as a troubled-cell indicator

Distributed Optimization and Control using Operator Splitting Methods