Submission information
Submission Number: 11
Submission ID: 8955
Submission UUID: 6e654fc2-3bae-48df-82a2-5d03e13dfc69
Submission URI: /form/submit-your-research-project
Submission View: /webform/submit_your_research_project/submissions/8955?token=CQJgwPhQBMSjuOF_LJq3NR_JQIgSDq5bd6J888T3tZg
Submission Update: /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
Simulate Ecological Models using Physics-informed Neural Networks
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.
Faculty, Student
- Name: Liang Kong
Email: lkong9@uis.edu
Phone: 2172067219
Link:
- Name: Christopher Denq
Email: cdenq2@uis.edu - Name: Abhi Soni
Email: asoni24@uis.edu - Name: Anthony Delligatti
Email: adell3@uis.edu
Not required