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Zhinv: Real-time hub-height wind field reconstruction using only local sparse observations
Zongwei Zhang, Chin Chun Ooi, Lianlei Lin +8
The high proportion of wind power connected to the grid places higher demands on fine-grained knowledge of regional wind fields. Since the wind information directly obtainable in a…
Meta-Inverse Physics-Informed Neural Networks for High-Dimensional Ordinary Differential Equations
Zhao Wei, Kenneth Hor Cheng Koh, Sheng Yuan Chin +3
Solving inverse problems in dynamical systems governed by high-dimensional coupled ordinary differential equations (ODEs) is a ubiquitous challenge in scientific machine learning.…
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…
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…