Physics-Informed Neural Networks (PINNs) Integrating Physics

Author

Kong, Liang

Publish Date

2025-07-07

Physics-Informed Neural Networks (PINNs) Integrating Physics and Machine Learning: A Synergistic Approach to Artificial Intelligence Education

The PINN architecture diagram — **PINN Architecture Diagram**. The PINN architecture enhances traditional models by incorporating a second (physics) penalty term into the loss function, improving accuracy and physical consistency

This is a simulation and plot generated using the EpiModel package with the ICM model. The simulation is stochastic and based on 20 randomly selected samples — **PINN Architecture Diagram**. The PINN architecture enhances traditional models by incorporating a second (physics) penalty term into the loss function, improving accuracy and physical consistency

Funding Source

Partially supported by Illinois Innovation Network (IIN), Sustaining Illinois Seed Funding

Competitive Scholarly Research Grants (CSRG)

Methods & Key Findings

This research develops novel PINN architectures to simulate complex processes, including species spread, damped oscillations, and disease dynamics, by integrating differential equations with neural networks. The project emphasizes model interpretability and physical consistency, yielding promising results for equations such as the SEIR model and the Fisher and Lotka-Volterra systems.

Collaborators & Students

Christopher Denq (Math major)
Abhi Soni (CS major)
Anthony Delligatti (CS graduate)

Publications & Presentations

Exploring SEIR Epidemiological Modeling using Physics-Informed Neural Networks (with Abhishek Soni)—Published and presented in the 2025 IEEE International Conference on Software Engineering and Artificial Intelligence (SEAI).
Integrating Physics and Machine Learning: A Synergistic Approach to Artificial Intelligence Education (with Anthony Delligatti)—Published and presented in the 2024 Workshop on Artificial Intelligence and Education (WAIE).
A Meshfree Deep Learning Approach for Numerical Solution of Differential Equations with Implementation in Python (with Christopher Denq)—under review.