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math.NA2026
Hard-constrained Physics-informed Neural Networks for Interface Problems
Seung Whan Chung, Stephen T. Castonguay, Sumanta Roy +3
Physics-informed neural networks (PINNs) have emerged as a flexible framework for solving partial differential equations, but interface problems remain challenging because continui…
math.NA2023★ 13 cited
A multifidelity approach to continual learning for physical systems
Amanda Howard, Yucheng Fu, Panos Stinis
We introduce a novel continual learning method based on multifidelity deep neural networks. This method learns the correlation between the output of previously trained models and t…