3 papers
cs.AI2026
Modularity-Free Conflict-Averse Training for Generalized PINNs
Heejo Kong, Beomchul Park, Sung-Jin Kim +1
Physics-informed neural networks (PINNs) have become a powerful framework for solving PDEs by embedding physical laws into differentiable objectives. Despite their advances, traini…
cs.AI2026
Compositional Meta-Learning for Mitigating Task Heterogeneity in Physics-Informed Neural Networks
Beomchul Park, Minsu Koh, Heejo Kong +1
Physics-informed neural networks (PINNs) approximate solutions of partial differential equations (PDEs) by embedding physical laws into the loss function. In parameterized PDE fami…
cs.LG2025
Integrating Locality-Aware Attention with Transformers for General Geometry PDEs
Minsu Koh, Beom-Chul Park, Heejo Kong +1
Neural operators have emerged as promising frameworks for learning mappings governed by partial differential equations (PDEs), serving as data-driven alternatives to traditional nu…