137 citations · 194 across the 16 of their papers we have counts for
5 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…
Amortized Multi-Objective Optimization Across Tasks with Generative Solution Modeling
Tingyang Wei, Jiao Liu, Abhishek Gupta +3
Many real-world applications require solving families of expensive multi-objective optimization problems~(EMOPs) under varying operational conditions. This can be formulated as par…
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…
Prompt Evolution for Generative AI: A Classifier-Guided Approach
Melvin Wong, Yew-Soon Ong, Abhishek Gupta +2
Synthesis of digital artifacts conditioned on user prompts has become an important paradigm facilitating an explosion of use cases with generative AI. However, such models often fa…