Data-driven forward-inverse problems for Yajima-Oikawa system using deep learning with parameter regularization

TL;DR

This paper enhances physics-informed neural networks with adaptive activation functions and regularization to accurately solve forward and inverse Yajima-Oikawa system problems, including rogue wave recovery and noise robustness.

Contribution

It introduces combined strategies of adaptive activation functions and $L^2$ regularization to improve PINN performance on noisy data for Yajima-Oikawa system inverse problems.

Findings

Successfully recovered three types of rogue waves using PINN.

Enhanced inverse problem solving with noise-robust training.

PINN with two strategies outperforms traditional methods.

Abstract

We investigate data-driven forward-inverse problems for Yajima-Oikawa (YO) system by employing two technologies which improve the performance of neural network in deep physics-informed neural network (PINN), namely neuron-wise locally adaptive activation functions and $L^2$ norm parameter regularization. Indeed, we not only recover three different forms of vector rogue waves (RWs) by means of three distinct initial-boundary value conditions in the forward problem of YO system, including bright-bright RWs, intermediate-bright RWs and dark-bright RWs, but also study the inverse problem of YO system by using training data with different noise intensity. In order to deal with the problem that the capacity of learning unknown parameters is not ideal when the PINN with only locally adaptive activation functions utilizes training data with noise interference in the inverse problem of YO system, thus we introduce $L^2$ norm regularization, which can drive the weights closer to origin, into PINN with locally adaptive activation functions, then find that the PINN model with two strategies shows amazing training effect by using training data with noise interference to investigate the inverse problem of YO system.