DeepONet-accelerated Bayesian inversion for moving boundary problems

TL;DR

This paper introduces a DeepONet-based surrogate model coupled with Bayesian inversion to efficiently and accurately estimate parameters in moving boundary problems, significantly advancing real-time monitoring and control in industrial processes.

Contribution

It presents a novel neural operator framework that generalizes across domains for moving boundary problems, enabling fast Bayesian inversion without retraining.

Findings

DeepONet surrogate accelerates inversion by several orders of magnitude.

Enables real-time, high-resolution parameter estimation in manufacturing processes.

Supports arbitrary sensor configurations without retraining.

Abstract

This work demonstrates that neural operator learning provides a powerful and flexible framework for building fast, accurate emulators of moving boundary systems, enabling their integration into digital twin platforms. To this end, a Deep Operator Network (DeepONet) architecture is employed to construct an efficient surrogate model for moving boundary problems in single-phase Darcy flow through porous media. The surrogate enables rapid and accurate approximation of complex flow dynamics and is coupled with an Ensemble Kalman Inversion (EKI) algorithm to solve Bayesian inverse problems. The proposed inversion framework is demonstrated by estimating the permeability and porosity of fibre reinforcements for composite materials manufactured via the Resin Transfer Moulding (RTM) process. Using both synthetic and experimental in-process data, the DeepONet surrogate accelerates inversion by several orders of magnitude compared with full-model EKI. This computational efficiency enables real-time, accurate, high-resolution estimation of local variations in permeability, porosity, and other parameters, thereby supporting effective monitoring and control of RTM processes, as well as other applications involving moving boundary flows. Unlike prior approaches for RTM inversion that learn mesh-dependent mappings, the proposed neural operator generalises across spatial and temporal domains, enabling evaluation at arbitrary sensor configurations without retraining, and represents a significant step toward practical industrial deployment of digital twins.