这篇论文搞了个叫NOTES的框架，用神经算子压缩设计空间，再用进化策略找最优，在纳米光子偏转器上效率超95%，比传统方法好。
论文提出NOTES方法，将DeepONet神经算子与CMA-ES进化策略结合，在紧凑潜空间中进行全局优化。该方法将纳米光子束偏转器的设计维度从256降至25，并实现超过95%的效率。在结构优化任务中，NOTES发现的合规性设计达到246。与CMA-ES、拓扑优化等基线相比，NOTES在性能和迁移性上均有提升。
Neural Operator-enabled Topology-informed Evolutionary Strategy for PDE-Constrained Optimization
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.