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Research Title: Simulate Ecological Models using Physics-informed Neural Networks
Projected Timeline
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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):
- Name: Liang Kong
  Email: lkong9@uis.edu
  Phone: 2172067219
  Link: https://liangkong.net/

Assistant Researcher(s):
- Name: Christopher Denq
  Email: cdenq2@uis.edu
- Name: Abhi Soni
  Email: asoni24@uis.edu
- Name: Anthony Delligatti
  Email: adell3@uis.edu

Department(s):
College of Health, Science and Technology (2600000694)

Keywords:
- Deep Ñî¹óåú´«Ã½ (2600004301)
- Physics-informed Neural network (2600004302)
- Computational Mathematics (2600004303)

Has this research project received IRB approval?: Not required