50 citations · 106 across the 13 of their papers we have counts for
5 papers · 1 filter
WGFINNs: Weak formulation-based GENERIC formalism informed neural networks
Jun Sur Richard Park, Auroni Huque Hashim, Siu Wun Cheung +2
Data-driven discovery of governing equations from noisy observations remains a fundamental challenge in scientific machine learning. While GENERIC formalism informed neural network…
Free-RBF-KAN: Kolmogorov-Arnold Networks with Adaptive Radial Basis Functions for Efficient Function Learning
Shao-Ting Chiu, Siu Wun Cheung, Ulisses Braga-Neto +2
Kolmogorov-Arnold Networks (KANs) offer a promising framework for approximating complex nonlinear functions, yet the original B-spline formulation suffers from significant computat…
Defining Foundation Models for Computational Science: A Call for Clarity and Rigor
Youngsoo Choi, Siu Wun Cheung, Youngkyu Kim +9
The widespread success of foundation models in natural language processing and computer vision has inspired researchers to extend the concept to scientific machine learning and com…
tLaSDI: Thermodynamics-informed latent space dynamics identification
Jun Sur Richard Park, Siu Wun Cheung, Youngsoo Choi +1
We propose a latent space dynamics identification method, namely tLaSDI, that embeds the first and second principles of thermodynamics. The latent variables are learned through an…
AdjointBackMapV2: Precise Reconstruction of Arbitrary CNN Unit's Activation via Adjoint Operators
Qing Wan, Siu Wun Cheung, Yoonsuck Choe
Adjoint operators have been found to be effective in the exploration of CNN's inner workings [1]. However, the previous no-bias assumption restricted its generalization. We overcom…