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
| Approved | Yes |
|---|---|
| Title | Solving differential equations with Physics-informed Neural Network |
| Category | Learning |
| Skill Level | Beginner, Intermediate |
| Description | 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. |
| Link to Resource | |
| Tags | neural-networks |
| Domain | ACCESS CSSN, Campus Champions, CAREERS, CCMNet, Great Plains, Kentucky, Northeast |
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