# Inverse Design in Nanophotonics via Representation Learning

arXiv: https://arxiv.org/abs/2507.00546 · HTML: https://mlsanity.com/paper/2507.00546/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 32 of its 149 references that are in the map.
- First posted on arXiv 1 Jul 2025.

## Builds on (most cited first, 30 of 32)

- [Physics-informed neural networks with hard constraints for inverse design](https://mlsanity.com/paper/2102.04626/) (2021)
- [Solver-in-the-Loop: Learning from Differentiable Physics to Interact with Iterative PDE-Solvers](https://mlsanity.com/paper/2007.00016/) (2020)
- [Discovering physical concepts with neural networks](https://mlsanity.com/paper/1807.10300/) (2018)
- [Multifidelity deep neural operators for efficient learning of partial differential equations with application to fast inverse design of nanoscale heat transport](https://mlsanity.com/paper/2204.06684/) (2022)
- [Physics-Informed Machine Learning: A Survey on Problems, Methods and Applications](https://mlsanity.com/paper/2211.08064/) (2022)
- [Deep neural networks for the evaluation and design of photonic devices](https://mlsanity.com/paper/2007.00084/) (2020)
- [On the use of Artificial Neural Networks in Topology Optimisation](https://mlsanity.com/paper/2208.02563/) (2022)
- [Global optimization of dielectric metasurfaces using a physics-driven neural network](https://mlsanity.com/paper/1906.04157/) (2019)
- [Active learning of deep surrogates for PDEs: Application to metasurface design](https://mlsanity.com/paper/2008.12649/) (2020)
- [A neural operator-based surrogate solver for free-form electromagnetic inverse design](https://mlsanity.com/paper/2302.01934/) (2023)
- [Physics-enhanced deep surrogates for partial differential equations](https://mlsanity.com/paper/2111.05841/) (2021)
- [NeurOLight: A Physics-Agnostic Neural Operator Enabling Parametric Photonic Device Simulation](https://mlsanity.com/paper/2209.10098/) (2022)
- [Mixed formulation of physics-informed neural networks for thermo-mechanically coupled systems and heterogeneous domains](https://mlsanity.com/paper/2302.04954/) (2023)
- [A multi-agentic framework for real-time, autonomous freeform metasurface design](https://mlsanity.com/paper/2503.20479/) (2025)
- [Deep learning approach based on dimensionality reduction for designing electromagnetic nanostructures](https://mlsanity.com/paper/1902.03865/) (2019)
- [Inverse design of photonic crystals through automatic differentiation](https://mlsanity.com/paper/2003.00379/) (2020)
- [Solving multiphysics-based inverse problems with learned surrogates and constraints](https://mlsanity.com/paper/2307.11099/) (2023)
- [Multi-objective and categorical global optimization of photonic structures based on ResNet generative neural networks](https://mlsanity.com/paper/2007.12551/) (2020)
- [Manifold Learning for Knowledge Discovery and Intelligent Inverse Design of Photonic Nanostructures: Breaking the Geometric Complexity](https://mlsanity.com/paper/2102.04454/) (2021)
- [All-optical graph representation learning using integrated diffractive photonic computing units](https://mlsanity.com/paper/2204.10978/) (2022)
- [HiLAB: A Hybrid Inverse-Design Framework](https://mlsanity.com/paper/2505.17491/) (2025)
- [Deep Learning Reveals Underlying Physics of Light-matter Interactions in Nanophotonic Devices](https://mlsanity.com/paper/1905.06889/) (2019)
- [Knowledge Discovery In Nanophotonics Using Geometric Deep Learning](https://mlsanity.com/paper/1909.07330/) (2019)
- [A single inverse-designed photonic structure that performs parallel computing](https://mlsanity.com/paper/2009.01187/) (2020)
- [Bayesian optimization with improved scalability and derivative information for efficient design of nanophotonic structures](https://mlsanity.com/paper/2101.02972/) (2021)
- [Tackling Multimodal Device Distributions in Inverse Photonic Design using Invertible Neural Networks](https://mlsanity.com/paper/2208.14212/) (2022)
- [Landslide Topology Uncovers Failure Movements](https://mlsanity.com/paper/2310.09631/) (2023)
- [PRDP: Progressively Refined Differentiable Physics](https://mlsanity.com/paper/2502.19611/) (2025)
- [Latent Space Optimal Transport for Generative Models](https://mlsanity.com/paper/1809.05964/) (2018)
- [MAPS: Multi-Fidelity AI-Augmented Photonic Simulation and Inverse Design Infrastructure](https://mlsanity.com/paper/2503.01046/) (2025)

## Built on by (most cited first, 7 of 7)

- [Harnessing AI for Inverse Partial Differential Equation Problems: Past, Present, and Prospects](https://mlsanity.com/paper/2605.16966/) (2026)
- [Neural surrogates for designing gravitational wave detectors](https://mlsanity.com/paper/2511.19364/) (2025)
- [Physics Enhanced Deep Surrogates for the Phonon Boltzmann Transport Equation](https://mlsanity.com/paper/2512.05976/) (2025)
- [Bayesian Optimization for Function-Valued Responses under Min-Max Criteria](https://mlsanity.com/paper/2512.07868/) (2025)
- [Gradient-Informed Bayesian and Interior Point Optimization for Efficient Inverse Design in Nanophotonics](https://mlsanity.com/paper/2602.18148/) (2026)
- [Harnessing Photonics for Machine Intelligence](https://mlsanity.com/paper/2604.10841/) (2026)
- [DDSNet: Dual-domain Symmetry-aware Network for PCSEL Property Prediction](https://mlsanity.com/paper/2607.24785/) (2026)

## Abstract (arXiv)

Inverse design in nanophotonics, the computational discovery of structures achieving targeted electromagnetic (EM) responses, has become a key tool for recent optical advances. Traditional intuition-driven or iterative optimization methods struggle with the inherently high-dimensional, non-convex design spaces and the substantial computational demands of EM simulations. Recently, machine learning (ML) has emerged to address these bottlenecks effectively. This review frames ML-enhanced inverse design methodologies through the lens of representation learning, classifying them into two categories: output-side and input-side approaches. Output-side methods use ML to learn a representation in the solution space to create a differentiable solver that accelerates optimization. Conversely, input-side techniques employ ML to learn compact, latent-space representations of feasible device geometries, enabling efficient global exploration through generative models. Each strategy presents unique trade-offs in data requirements, generalization capacity, and novel design discovery potentials. Hybrid frameworks that combine physics-based optimization with data-driven representations help escape poor local optima, improve scalability, and facilitate knowledge transfer. We conclude by highlighting open challenges and opportunities, emphasizing complexity management, geometry-independent representations, integration of fabrication constraints, and advancements in multiphysics co-designs.