Submission information
Submission Number: 211
Submission ID: 4068
Submission UUID: a277bbbe-6278-44e4-aaef-21fe2ef7122a
Submission URI: /form/resource
Created: Fri, 09/29/2023 - 12:14
Completed: Fri, 09/29/2023 - 12:14
Changed: Fri, 03/14/2025 - 11:43
Remote IP address: 18.29.105.203
Submitted by: Bao Nguyen
Language: English
Is draft: No
Webform: Knowledge Base Resources
Yes
Solving differential equations with Physics-informed Neural Network
Learning
Differential equations, the backbone of countless physical phenomena, have traditionally been solved using numerical methods or analytical techniques. However, the advent of deep learning introduces an intriguing alternative: Physics-Informed Neural Networks (PINNs). By leveraging the representational power of neural networks and integrating physical laws (like differential equations), PINNs offer a novel approach to solving complex problems. This guide walks through an implementation of a PINN to solve DEs such as the logistic equation.
{Empty}