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Data Analysis and Problem Solving Seminar
Data Analysis and Problem Solving Seminar
Solving and Discovering PDEs with Physics-Informed Neural Networks
Solving and Discovering PDEs with Physics-Informed Neural Networks
Organizer
Speaker
Zhuoyang Zhao
Time
Friday, August 7, 2026 3:00 PM - 4:00 PM
Venue
A3-3-301
Online
Zoom 204 323 0165
(BIMSA)
Abstract
This presentation introduces physics-informed neural networks (PINNs) for solving forward and inverse problems involving nonlinear partial differential equations. PINNs approximate the unknown solution with a neural network and include the governing equation in the loss function through automatic differentiation. Both continuous-time and discrete-time formulations are discussed, with emphasis on how Runge-Kutta methods connect different time snapshots. Several examples, including Burgers, Schrodinger, Allen-Cahn, Navier-Stokes, and KdV equations, are used to show how PINNs recover solutions and identify unknown parameters from limited data.
Speaker Intro
Zhao Zhuoyang is a first-year Ph.D. student in a joint Program between BIMSA and Renmin University of China, majoring in Mathematics, under the supervision of Professor Zhang Xiaoming.