Phase-Retrieval-Based Physics-Informed Neural Networks For Acoustic Magnitude Field Reconstruction

TL;DR

This paper introduces a novel phase-retrieval-based physics-informed neural network that estimates acoustic magnitude fields from sparse measurements without requiring phase data, leveraging PDE constraints for improved sound field reconstruction.

Contribution

It extends PINNs to handle magnitude-only acoustic data by incorporating phase retrieval, enabling accurate sound field estimation without phase measurements.

Findings

Effective magnitude field reconstruction demonstrated

Outperforms traditional methods in sparse measurement scenarios

Validates approach through experimental results

Abstract

We propose a method for estimating the magnitude distribution of an acoustic field from spatially sparse magnitude measurements. Such a method is useful when phase measurements are unreliable or inaccessible. Physics-informed neural networks (PINNs) have shown promise for sound field estimation by incorporating constraints derived from governing partial differential equations (PDEs) into neural networks. However, they do not extend to settings where phase measurements are unavailable, as the loss function based on the governing PDE relies on phase information. To remedy this, we propose a phase-retrieval-based PINN for magnitude field estimation. By representing the magnitude and phase distributions with separate networks, the PDE loss can be computed based on the reconstructed complex amplitude. We demonstrate the effectiveness of our phase-retrieval-based PINN through experimental evaluation.