3 papers
cs.LG2026
When and Why Adversarial Training Improves PINNs: A Neural Tangent Kernel Perspective
Yuan-dong Cao, Chi Chiu SO, Jun-Min Wang +1
Physics-informed neural networks (PINNs) are powerful surrogates for differential equations but are notoriously difficult to train due to spectral bias, stiffness, and poor accurac…
cs.AI2025
Are Large Language Models Capable of Deep Relational Reasoning? Insights from DeepSeek-R1 and Benchmark Comparisons
Chi Chiu So, Yueyue Sun, Jun-Min Wang +3
How far are Large Language Models (LLMs) in performing deep relational reasoning? In this paper, we evaluate and compare the reasoning capabilities of three cutting-edge LLMs, name…
cs.NE2024
Higher-order-ReLU-KANs (HRKANs) for solving physics-informed neural networks (PINNs) more accurately, robustly and faster
Chi Chiu So, Siu Pang Yung
Finding solutions to partial differential equations (PDEs) is an important and essential component in many scientific and engineering discoveries. One of the common approaches empo…