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This research presents a hybrid framework combining neural operators (DeepONet) with evolutionary strategies to solve high-dimensional inverse design problems governed by partial differential equations. By mapping complex physical systems into compact latent spaces, the method achieves significant dimensionality reduction while maintaining robustness across unseen operating conditions in applications like nanophotonics and structural mechanics. The work advances machine learning's utility as a surrogate for computationally expensive physics simulations without requiring explicit training on specific datasets.
Neural Operator-enabled Topology-informed Evolutionary Strategy for PDE-Constrained Optimization
arXiv:2607.07682v2 Announce Type: replace Abstract: The inverse design of physical systems governed by partial differential equations is computationally demanding due to the high dimensionality and non-convexity of design spaces. Generative models for inverse design often lack robustness and transferability, whereas evolutionary strategies are robust but struggle in high-dimensional spaces. This paper introduces a Neural Operator-enabled Topology-informed Evolutionary Strategy (NOTES) that integrates dimensionality reduction, representation learning, and evolutionary optimization for efficient and transferable inverse design. NOTES couples a DeepONet-based neural operator with the Covariance Matrix Adaptation Evolution Strategy (CMA-ES) to perform global optimization in a compact latent space that encodes topology-aware priors while discovering high-performance designs for unseen operating conditions. Applied to nanophotonic beam-deflector inverse design governed by Maxwell's equations, NOTES reduces the design dimensionality from 256 to 25 and consistently achieves over 95 percent efficiency, outperforming CMA-ES, topology optimization, and other baselines. Applied to structural optimization, NOTES discovers designs that achieve compliance down to 246. By decoupling topology learning of a DeepONet from the governing physics in a PDE solver, NOTES provides a flexible and transferable framework for the inverse design of physical systems.
Abstract:The inverse design of physical systems governed by partial differential equations is computationally demanding due to the high dimensionality and non-convexity of design spaces. Generative models for inverse design often lack robustness and transferability, whereas evolutionary strategies are robust but struggle in high-dimensional spaces. This paper introduces a Neural Operator-enabled Topology-informed Evolutionary Strategy (NOTES) that integrates dimensionality reduction, representation learning, and evolutionary optimization for efficient and transferable inverse design. NOTES couples a DeepONet-based neural operator with the Covariance Matrix Adaptation Evolution Strategy (CMA-ES) to perform global optimization in a compact latent space that encodes topology-aware priors while discovering high-performance designs for unseen operating conditions. Applied to nanophotonic beam-deflector inverse design governed by Maxwell's equations, NOTES reduces the design dimensionality from 256 to 25 and consistently achieves over 95 percent efficiency, outperforming CMA-ES, topology optimization, and other baselines. Applied to structural optimization, NOTES discovers designs that achieve compliance down to 246. By decoupling topology learning of a DeepONet from the governing physics in a PDE solver, NOTES provides a flexible and transferable framework for the inverse design of physical systems.
Machine Learning (cs.LG)
arXiv:2607.07682 [cs.LG]
(or arXiv:2607.07682v2 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2607.07682 Focus to learn more arXiv-issued DOI via DataCite
https://doi.org/10.1109/TEVC.2026.3708401 Focus to learn more DOI(s) linking to related resources
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