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Data Analysis and Problem Solving Seminar
Data Analysis and Problem Solving Seminar
Representation Model Modifications in Physics-Informed Machine Learning
Representation Model Modifications in Physics-Informed Machine Learning
Organizer
Speaker
Time
Friday, June 5, 2026 3:00 PM - 4:00 PM
Venue
A3-1-301
Online
Zoom 204 323 0165
(BIMSA)
Abstract
This report mainly introduces the core content of Section 3.1, “Representation Model Modifications,” from the review paper From PINNs to PIKANs: Recent Advances in Physics-Informed Machine Learning. Physics-informed machine learning methods generally consist of three components: a representation model used to approximate the solution of the governing equation, governing equations used to describe the underlying physical laws, and an optimization process used to train the model. Among these components, the representation model directly determines the network’s ability to express complex physical fields, boundary conditions, and multiscale features. Therefore, it is an important entry point for improving the performance of PINNs. Section 3.1 of the paper systematically reviews recent improvement strategies at the level of representation models, including input and output transformations, feature expansions, hard constraint embedding, MLP architecture improvements, the application of Kolmogorov-Arnold networks in PIML, residual connections, and model decomposition methods.