Physics-informed neural networks
id:
physics-informed-neural-networks-178-6238309
title:
Physics-informed neural networks
text:
Physics-informed neural networks (PINNs), also referred to as Theory-Trained Neural Networks (TTNs), are a type of universal function approximators that can embed the knowledge of any physical laws that govern a given data-set in the learning process, and can be described by partial differential equations (PDEs). They overcome the low data availability of some biological and engineering systems that makes most state-of-the-art machine learning techniques lack robustness, rendering them ineffecti
brand slug:
wiki
category slug:
encyclopedia
description:
Technique to solve partial differential equations
original url:
https://en.wikipedia.org/wiki/Physics-informed_neural_networks
date created:
2021-06-14T09:20:21Z
date modified:
2024-09-04T11:31:52Z
main entity:
{"identifier":"Q107285102","url":"https://www.wikidata.org/entity/Q107285102"}
image:
{"content_url":"https://upload.wikimedia.org/wikipedia/commons/9/90/Physics-informed_nerural_networks.png","width":1280,"height":720}
fields total:
13
integrity:
16