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DeepONet-accelerated Bayesian inversion for moving boundary problems
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DOI：10.1016/j.cma.2026.118988.png)
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Abstract
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This work demonstrates that neural operator learning provides a powerful and flexible framework for building fast, accurate emulators of moving boundary problems, 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 a moving boundary problem 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.
Keywords:
Moving boundary problems
Neural operators
DeepONet
Bayesian inverse problem
Ensemble Kalman inversion
Resin transfer moulding
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