Bio:

I am currently a Ph.D. candidate in the Department of Civil and Environmental Engineering at the University of Illinois at Urbana-Champaign. I received my M.S. in Structural Engineering from the University of Illinois in 2022 and my B.Eng. in Civil Engineering from Chulalongkorn University in 2020.

My research focuses on scientific machine learning for earthquake physics, with an emphasis on physics-informed machine learning for both forward and inverse modeling. I also work on operator learning techniques to accelerate large-scale earthquake simulations.

Projects:

·       Operator learning to model and accelerate the simulation of fault dynamics.

This project focuses on developing operator learning techniques to model and accelerate the simulation of fault dynamics. By learning the underlying operators that govern complex earthquake processes, this approach enables faster and more efficient simulations while maintaining physical accuracy. It offers a promising alternative to traditional numerical solvers for large-scale problems.

·       Physics-informed machine learning for parametric inversion of fault properties.

This work aims to estimate key fault properties, such as frictional parameters or stiffness, from observed or simulated data. By embedding physical constraints into machine learning models, the approach improves the robustness and interpretability of parametric inversion.

·       Physics-informed machine learning for forward modeling of earthquake processes.

This work focuses on using machine learning models guided by physical laws to simulate earthquake behavior. The goal is to capture complex, nonlinear fault responses with higher accuracy and generalizability, enabling better understanding and prediction of seismic events.

Presentations:

[1] Tainpakdipat, N., & Elbanna, A. (2024, May 28–31). Predicting earthquake fault dynamics and parametric identification through Physics-Informed Neural Networks. Engineering Mechanics Institute Conference and Probabilistic Mechanics and Reliability Conference (EMI/PMC 2024), Chicago, IL.

Poster Presentations:

[1] Tainpakdipat, N., & Elbanna, A. (2024, September 8–11). Fault dynamics parameter identification using Physics-Informed Neural Networks. Statewide California Earthquake Center Annual Meeting, Palm Springs, CA.

[2] Tainpakdipat, N., Zhao, C., Abdelmeguid, M., & Elbanna, A. (2024, December 9–13). Physics-Informed Neural Networks and Fourier Neural Operators for Forward and Inverse Problems in Earthquake Physics. American Geophysical Union (AGU) Meeting 2024, Washington, D.C.

[3] Chourasia A., Youn C., Silva F., Olsen B., Zhao C., Yun J., Tainpakdipat N., Maechling P., May D., Elbanna A., Gabriel A., Ben-Zion Y. (2024, December 9–13). Quakeworx: A New Science Gateway for Earthquake Simulation and Data Analysis. American Geophysical Union (AGU) Meeting 2024, Washington, D.C.

Teaching Experience:

I have been a Graduate Teaching Assistant for the following courses:

CEE572 Earthquake Engineering                                          Spring 2025

CEE468 Prestressed Concrete                                                    Spring 2023

CEE460 Steel Structures I                                                        Fall 2022, Fall 2024

CEE360 Structural Engineering                                                  Fall 2021

Awards and Honors:

Teachers Ranked as Excellent                                                Spring 2023