Modularity-Free Conflict-Averse Training for Generalized PINNs

Researchers identify a critical failure mode in Physics-Informed Neural Networks (PINNs) where overparameterized models self-partition into task-exclusive modules that impede training convergence. They introduce ModSync, a novel framework combining structural optimization with conflict-averse training to prevent capacity-driven failures and achieve state-of-the-art accuracy across PDE benchmarks.

Physics-Informed Neural Networks represent a significant advancement in computational science, enabling neural networks to solve partial differential equations by embedding physical laws directly into loss functions. However, recent research reveals a counterintuitive problem: as model capacity increases, training becomes less stable despite conventional wisdom suggesting larger networks should perform better. This capacity-induced failure occurs because overparameterized PINNs develop functional modularity, where different neural network components specialize exclusively in solving different physics objectives, effectively isolating themselves from one another.

This discovery builds on ongoing efforts to stabilize PINN training through conflict-averse optimization, which manages competing gradient signals from residual and boundary condition losses. Previous approaches addressed gradient interference at a fundamental level, but failed to account for how network architecture itself adapts to suppress inter-objective interaction as capacity grows. The ModSync framework tackles this architectural pathology by penalizing task-exclusive connections while preserving pathways that promote cross-objective coupling.

For the scientific computing and machine learning communities, this work has substantial implications. PINNs are increasingly deployed in engineering, climate modeling, and drug discovery—domains where solution accuracy directly impacts real-world outcomes. ModSync's ability to prevent capacity-driven failures and maintain robust convergence across diverse PDE benchmarks enhances the reliability of these applications. The research demonstrates that structural constraints on neural network organization can be as important as loss function design in training complex physics-informed models.

Looking forward, this research likely catalyzes deeper investigation into modularity in overparameterized networks beyond the physics-informed context, potentially influencing broader deep learning architecture design principles.

• →PINNs experience functional modularity in large models, where network components self-specialize into task-exclusive modules that hinder convergence.
• →ModSync framework successfully prevents capacity-driven failures by penalizing task-exclusive connections while preserving objective-coupling pathways.
• →The research challenges assumptions that larger model capacity uniformly improves PINN training robustness and accuracy.
• →State-of-the-art results across diverse PDE benchmarks demonstrate ModSync's effectiveness for maintaining cross-objective coupling in overparameterized networks.
• →Findings have implications for scientific computing applications relying on PINNs in engineering, climate modeling, and computational physics.