LSTM-PINN: An Hybrid Method for Prediction of Steady-State Electrohydrodynamic Flow

TL;DR

This paper introduces a hybrid LSTM-PINN model that improves the prediction of steady-state electrohydrodynamic flows by enhancing convergence, stability, and accuracy over traditional PINNs, with applications in microfluidics.

Contribution

The paper presents a novel hybrid LSTM-PINN framework that better captures spatial correlations in electrohydrodynamic flow modeling, addressing limitations of conventional PINNs.

Findings

LSTM-PINN outperforms traditional PINNs in convergence and accuracy.

The hybrid model demonstrates improved numerical stability.

Enhanced computational efficiency in modeling electrohydrodynamic flows.

Abstract

Physics-Informed Neural Networks (PINNs) have demonstrated considerable success in solving complex fluid dynamics problems. However, their performance often deteriorates in regimes characterized by steep gradients, intricate boundary conditions, and stringent physical constraints, leading to convergence failures and numerical instabilities. To overcome these limitations, we propose a hybrid framework that integrates Long Short-Term Memory (LSTM) networks into the PINN architecture, enhancing its ability to capture spatial correlations in the steady-state velocity field of a two-dimensional charged fluid under an external electric field. Our results demonstrate that the LSTM-enhanced PINN model significantly outperforms conventional Multilayer Perceptron (MLP)-based PINNs in terms of convergence rate, numerical stability, and predictive accuracy. This innovative approach offers improved computational efficiency and reliability for modeling electrohydrodynamic flows, providing new insights and strategies for applications in microfluidics and nanofluidics.