Modularity-Free Conflict-Averse Training for Generalized PINNs

2026-06-18 · Source: Artificial Intelligence · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Mathematics & Computational Sciences, Engineering & Applied Sciences · Depth: Expert, quick

Summary

Physics-informed neural networks (PINNs) face significant training fragility, particularly as model capacity increases, where existing conflict-averse optimization methods lose effectiveness. Researchers identify a "capacity-induced failure mode" in overparameterized networks, leading to functional modularity. This modularity causes networks to self-partition into task-exclusive modules, suppressing crucial cross-objective interaction and impeding convergence to Pareto-stationary points. To counter this, a novel framework called Modular-Sparsity Synchronization (ModSync) is proposed. ModSync integrates structural optimization into conflict-averse training by specifically penalizing task-exclusive connections while actively preserving pathways that promote interaction. Extensive experiments across diverse PDE benchmarks demonstrate that ModSync consistently prevents these capacity-driven failures, maintains robust cross-objective coupling, and achieves high accuracy.

Key takeaway

For Machine Learning Engineers developing large-scale Physics-informed Neural Networks, you should be aware that increasing model capacity can paradoxically degrade training stability and convergence. Your existing conflict-averse optimization schemes may fail due to functional modularity. Consider implementing ModSync, which integrates structural optimization to prevent these capacity-driven failures, ensuring robust cross-objective coupling and higher accuracy in your PINN applications.

Key insights

Overparameterized PINNs suffer from functional modularity, which ModSync overcomes by synchronizing structural optimization.

Principles

• Increased PINN capacity can hinder convergence.
• Functional modularity suppresses cross-objective interaction.
• Penalizing task-exclusive connections improves PINN training.

Method

ModSync integrates structural optimization into conflict-averse training. It penalizes task-exclusive connections while preserving interaction-promoting pathways to prevent capacity-driven failures.

In practice

• Implement structural optimization in PINN training.
• Penalize task-exclusive connections in overparameterized networks.
• Use ModSync for improved large-scale PINN accuracy.

Topics

• Physics-informed Neural Networks
• Conflict-Averse Optimization
• Model Capacity
• Functional Modularity
• Structural Optimization
• ModSync

Code references

• heejokong/ModSync

Best for: AI Scientist, Machine Learning Engineer, Research Scientist

Related on AIssential

• PINN loss functions: why physics-informed networks often fail to train — PINNs struggle to train due to severe gradient imbalance and scale differences among their multi-term loss functions.
• BandPO: Bridging Trust Regions and Ratio Clipping via Probability-Aware Bounds for LLM Reinforcement Learning — BandPO dynamically adjusts policy update bounds to prevent entropy collapse and improve exploration in LLM reinforcement learning.
• Response time of lateral predictive coding and benefits of modular structures — LPC networks can achieve minimal response times and modularity without sacrificing energetic efficiency or feature detection.
• Dual-Network PINNs for Optimal Control: A Reproducible Benchmark on the Mass-Spring-Damper System — A dual-network PINN formulation achieves high accuracy in optimal control by exactly enforcing boundary conditions, offering a clear, reproducible alternative to classical methods.
• Boosting Multimodal Federated Learning via Chained Modality Optimization — FedMChain mitigates modality competition in MMFL via phased optimization and sign-guided aggregation for improved performance and efficiency.
• Robust Parameter and State Estimation in Multiscale Neuronal Systems Using Physics-Informed Neural Networks — PINNs offer robust parameter and state estimation in multiscale neuronal systems, overcoming limitations of traditional methods.

Open in AIssential →

Editorial summary, takeaway, and curation by AIssential. Original article published by Artificial Intelligence.