Evolutionary Two-Stage Hyperparameter Optimization Strategies for Physics-Informed Neural Networks
arXiv:2606.20442v1 Announce Type: new Abstract: Physics-Informed Neural Networks (PINNs) solve Partial Differential Equations (PDEs) by embedding physical laws into neural network training. However, their performance suffers from unstable convergence, training plateaus, and strong sensitivity to architectural and optimization hyperparameters due to the highly non-convex and multi-term structure of the physics-informed loss. In this setting, the outer-loop hyperparameter search is a noisy and black-box optimization problem over heterogeneous parameters, where classical local or gradient-based s
The increasing sophistication and application of AI in scientific discovery, particularly in fields like physics and engineering, necessitates advancements in AI training methodologies.
Improving the stability and performance of Physics-Informed Neural Networks (PINNs) can accelerate scientific discovery and engineering design by enabling more accurate and robust simulations and predictions.
The development of more effective hyperparameter optimization strategies for PINNs will lead to their broader adoption and more reliable application in complex scientific and engineering problems.
- · AI researchers
- · Engineers and scientists
- · Industries relying on simulations (e.g., aerospace, energy)
- · Traditional numerical methods (in some applications)
- · Inefficient AI training techniques
More accurate and faster solutions to Partial Differential Equations (PDEs) in various domains.
Reduced R&D cycles and costs for complex engineering and scientific challenges.
New product development and scientific breakthroughs previously unfeasible due to computational constraints.
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Read at arXiv cs.LG