Compositional Meta-Learning for Mitigating Task Heterogeneity in Physics-Informed Neural Networks

📰 ArXiv cs.AI

Learn how compositional meta-learning mitigates task heterogeneity in physics-informed neural networks, improving cross-task transfer and reducing retraining costs

advanced Published 1 May 2026

Action Steps

Apply compositional meta-learning to physics-informed neural networks to reduce task heterogeneity
Use meta-learning algorithms to learn task-agnostic representations
Train individual networks for each task using the learned representations
Evaluate the performance of the compositional meta-learning approach on parameterized PDE families
Compare the results with traditional training methods to assess the benefits of compositional meta-learning

Who Needs to Know This

Researchers and engineers working with physics-informed neural networks can benefit from this approach to improve model performance and efficiency in tasks with varying parameters or conditions

Key Insight

💡 Compositional meta-learning can effectively mitigate task heterogeneity in physics-informed neural networks, enabling more efficient and accurate solutions for parameterized PDE families

Key Takeaways

Learn how compositional meta-learning mitigates task heterogeneity in physics-informed neural networks, improving cross-task transfer and reducing retraining costs

Full Article

Title: Compositional Meta-Learning for Mitigating Task Heterogeneity in Physics-Informed Neural Networks

Abstract:
arXiv:2604.26999v1 Announce Type: new Abstract: 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 retraini

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