Submission information
Submission Number: 11
Submission ID: 8955
Submission UUID: 6e654fc2-3bae-48df-82a2-5d03e13dfc69
Submission URI: /zh-hans/form/submit-your-research-project
Submission View: /zh-hans/webform/submit_your_research_project/submissions/8955?token=CQJgwPhQBMSjuOF_LJq3NR_JQIgSDq5bd6J888T3tZg
Submission Update: /zh-hans/form/submit-your-research-project?token=CQJgwPhQBMSjuOF_LJq3NR_JQIgSDq5bd6J888T3tZg
Created: Sat, 09/28/2024 - 12:33
Completed: Sat, 09/28/2024 - 12:48
Changed: Sat, 09/28/2024 - 12:48
Remote IP address: 10.64.6.7
Submitted by: Anonymous
Language: English
Is draft: No
Webform: Submit Your Research Project
| Research Title | Simulate Ecological Models using Physics-informed Neural Networks |
|---|---|
| Start Date | Wed, 08/16/2023 - 00:00 |
| End Date | Fri, 07/31/2026 - 00:00 |
| Research Abstract | This project highlights the need for interdisciplinary AI education, focusing on integrating physics and machine learning. While Physics-Informed Neural Networks (PINNs) demonstrate the importance of incorporating physical laws into AI models, current curricula often overlook this. We propose the designs that combine a type of Ecological differential equations and machine learning to equip students with the skills to create models that are both data-driven and grounded in physics. |
| Project Type | Faculty, Student |
| Lead Researcher(s) |
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| Assistant Researcher(s) |
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| Department(s) | College of Health, Science and Technology |
| Keywords | |
| Has this research project received IRB approval? | Not required |