arXiv:2606.20156v1 Announce Type: new Abstract: Physics-informed neural networks (PINNs) have become a powerful framework for solving PDEs by embedding physical laws into differentiable objectives. Despite their advances, training PINNs remains fragile: recent conflict-averse optimization schemes alleviate gradient interference between residual and boundary losses, but we show that their effectiveness deteriorates as model capacity increases. In this paper, we identify a capacity-induced failure mode, where overparameterized networks undergo functional modularity, self-partitioning into task-e
This paper identifies a critical limitation in current PINN training methodologies, emerging as the field pushes towards more complex models and broader applications of AI in scientific computing.
Improved training stability and effectiveness in Physics-informed neural networks can accelerate scientific discovery and engineering solutions by reliably integrating physical laws into AI models.
The understanding of PINN failure modes changes, potentially leading to more robust and scalable AI models for scientific and engineering problems beyond the current capacity limitations.
- · Researchers in scientific AI
- · Engineering simulation software developers
- · Industries relying on complex PDE solving
- · Developers of current conflict-averse optimization schemes
- · Organizations using PINNs at scale without robust training methods
More stable and performant Physics-informed neural networks will be developed, improving accuracy in various scientific and engineering applications.
This could accelerate the adoption of AI for complex simulations, potentially reducing the need for traditional computational fluid dynamics or finite element analysis.
Fundamental scientific breakthroughs in fields like materials science or climate modeling could occur faster due to more reliable and scalable AI tools.
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Read at arXiv cs.AI