28 citations · 31 across the 9 of their papers we have counts for
8 papers · 1 filter
Transferable Physics-Informed Representations via Closed-Form Head Adaptation
Jian Cheng Wong, Isaac Yin Chung Lai, Pao-Hsiung Chiu +3
Physics-informed neural networks (PINNs) have garnered significant interest for their potential in solving partial differential equations (PDEs) that govern a wide range of physica…
Out-of-Distribution Generalization for Neural Physics Solvers
Zhao Wei, Chin Chun Ooi, Jian Cheng Wong +3
Neural physics solvers are increasingly used in scientific discovery, given their potential for rapid in silico insights into physical, materials, or biological systems and their l…
Physics-Informed Uncertainty Enables Reliable AI-driven Design
Tingkai Xue, Chin Chun Ooi, Yang Jiang +5
Inverse design is a central goal in much of science and engineering, including frequency-selective surfaces (FSS) that are critical to microelectronics for telecommunications and o…
Evolvable Conditional Diffusion
Zhao Wei, Chin Chun Ooi, Abhishek Gupta +4
This paper presents an evolvable conditional diffusion method such that black-box, non-differentiable multi-physics models, as are common in domains like computational fluid dynami…
Multi-level datasets training method in Physics-Informed Neural Networks
Yao-Hsuan Tsai, Hsiao-Tung Juan, Pao-Hsiung Chiu +1
Physics-Informed Neural Networks have emerged as a promising methodology for solving PDEs, gaining significant attention in computer science and various physics-related fields. Des…
Design of Turing Systems with Physics-Informed Neural Networks
Jordon Kho, Winston Koh, Jian Cheng Wong +2
Reaction-diffusion (Turing) systems are fundamental to the formation of spatial patterns in nature and engineering. These systems are governed by a set of non-linear partial differ…