An Evolutionary Multi-objective Optimization for Replica-Exchange-based Physics-informed Operator Learning Network

• URL: http://arxiv.org/abs/2509.00663v1
• Date: Sun, 31 Aug 2025 02:17:59 GMT
• Title: An Evolutionary Multi-objective Optimization for Replica-Exchange-based Physics-informed Operator Learning Network
• Authors: Binghang Lu, Changhong Mou, Guang Lin,
• Abstract summary: We propose an evolutionary Multi-objective Optimization for Replica-based Physics-informed Operator learning Network.<n>Our framework consistently outperforms the general operator learning methods in accuracy, noise, and the ability to quantify uncertainty.
• Score: 7.1950116347185995
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In this paper, we propose an evolutionary Multi-objective Optimization for Replica-Exchange-based Physics-informed Operator learning Network, which is a novel operator learning network to efficiently solve parametric partial differential equations. In forward and inverse settings, this operator learning network only admits minimum requirement of noisy observational data. While physics-informed neural networks and operator learning approaches such as Deep Operator Networks and Fourier Neural Operators offer promising alternatives to traditional numerical solvers, they struggle with balancing operator and physics losses, maintaining robustness under noisy or sparse data, and providing uncertainty quantification. The proposed framework addresses these limitations by integrating: (i) evolutionary multi-objective optimization to adaptively balance operator and physics-based losses in the Pareto front; (ii) replica exchange stochastic gradient Langevin dynamics to improve global parameter-space exploration and accelerate convergence; and (iii) built-in Bayesian uncertainty quantification from stochastic sampling. The proposed operator learning method is tested numerically on several different problems including one-dimensional Burgers equation and the time-fractional mixed diffusion-wave equation. The results indicate that our framework consistently outperforms the general operator learning methods in accuracy, noise robustness, and the ability to quantify uncertainty.

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