Compositional Meta-Learning for Mitigating Task Heterogeneity in Physics-Informed Neural Networks
Physics-informed neural networks (PINNs) approximate solutions of partial differential equations (PDEs) by embedding physical laws into the loss function. In parameterized PDE families, variations in coefficients or boundary/initial conditions define distinct tasks. This makes training individual PINNs for each task computationally prohibitive, while cross-task transfer can be sensitive to task heterogeneity. While meta-learning can reduce retraining cost, existing methods often rely on a single global initialization and may suffer from negative transfer, particularly under feature-scarce coordinate inputs and limited training-task availability. We propose the Learning-Affinity Adaptive M...
| Authors | Beomchul Park, Minsu Koh, Heejo Kong et al. |
|---|---|
| Primary category | cs.AI |
| Published | 29 April 2026 |
| Last updated at source | 29 April 2026 |
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