Physics-Informed Latent Neural Operator for Real-time Predictions of Complex Physical Systems

• URL: http://arxiv.org/abs/2501.08428v2
• Date: Fri, 13 Jun 2025 01:08:41 GMT
• Title: Physics-Informed Latent Neural Operator for Real-time Predictions of Complex Physical Systems
• Authors: Sharmila Karumuri, Lori Graham-Brady, Somdatta Goswami,
• Abstract summary: We propose PI-Latent-NO, a physics-informed latent neural operator framework that integrates governing physics directly into the learning process.<n>Our architecture features two coupled DeepONets trained end-to-end: a Latent-DeepONet that learns a low-dimensional representation of the solution, and a Reconstruction-DeepONet that maps this latent representation back to the physical space.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Deep operator network (DeepONet) has shown significant promise as surrogate models for systems governed by partial differential equations (PDEs), enabling accurate mappings between infinite-dimensional function spaces. However, for complex, high-dimensional systems, these models often require heavily overparameterized networks, leading to long training times and convergence difficulties. Latent DeepONet addresses some of these challenges by introducing a two-step approach: first learning a reduced latent space using a separate model, followed by operator learning within this latent space. While efficient, this method is inherently data-driven and lacks mechanisms for incorporating physical laws, limiting its robustness and generalizability in data-scarce settings. In this work, we propose PI-Latent-NO, a physics-informed latent neural operator framework that integrates governing physics directly into the learning process. Our architecture features two coupled DeepONets trained end-to-end: a Latent-DeepONet that learns a low-dimensional representation of the solution, and a Reconstruction-DeepONet that maps this latent representation back to the physical space. By embedding PDE constraints into the training via automatic differentiation, our method eliminates the need for labeled training data and ensures physics-consistent predictions. The proposed framework is both memory and compute-efficient, exhibiting near-constant scaling with problem size and demonstrating significant speedups over traditional physics-informed operator models. We validate our approach on a range of high-dimensional parametric PDEs, showcasing its accuracy, scalability, and suitability for real-time prediction in complex physical systems.

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