High-Fidelity Reconstruction of Charge Boundary Layers and Sharp Interfaces in Electro-Thermal-Convective Flows via Residual-Attention PINNs

TL;DR

This paper introduces RA-PINN, a neural network architecture with residual attention, that accurately reconstructs sharp interfaces and boundary layers in electro-thermal-convective flows, overcoming limitations of traditional PINNs.

Contribution

The paper presents a novel RA-PINN architecture that embeds gated attention within residual networks to improve local sensitivity to steep physical gradients in flow modeling.

Findings

RA-PINN reduces localized errors in boundary layer reconstruction.

It faithfully preserves interface topologies in electrohydrodynamic scenarios.

The method outperforms standard and recurrent PINN baselines.

Abstract

Accurate reconstruction of localized extreme structures remains a critical bottleneck in the physics-informed modeling of electro-thermal-convective flows. Although conventional physics-informed neural networks effectively capture smooth global dynamics, they frequently suffer from numerical diffusion and distortion when attempting to resolve sharp charge boundary layers or abrupt multiphase interfaces. To address these limitations, we propose a Residual-Attention Physics-Informed Neural Network (RA-PINN) that embeds gated attention modulation within a residual feature framework to adaptively enhance local sensitivity to steep physical gradients. The proposed architecture is rigorously evaluated against standard and recurrent network baselines using canonical electrohydrodynamic scenarios, encompassing near-electrode exponential boundary layers and sharply concentrated charge fields. Quantitative analyses demonstrate that the RA-PINN significantly reduces localized errors and faithfully preserves critical interface topologies without compromising the global consistency dictated by the coupled governing equations. Ultimately, this methodology establishes a highly robust predictive framework for resolving complex interfacial and boundary layer phenomena in advanced fluid dynamics applications.