HiLAB: A Hybrid Inverse-Design Framework
Reza Marzban, Hamed Abiri, Raphael Pestourie, Ali Adibi

TL;DR
HiLAB introduces a hybrid inverse-design framework combining topological optimization, a variational autoencoder, and Bayesian search to efficiently design nanophotonic devices with fewer simulations and higher robustness.
Contribution
The paper presents a novel inverse-design paradigm that integrates multiple optimization techniques and machine learning to improve design efficiency and versatility in nanophotonics.
Findings
Reduces total electromagnetic simulations by over an order of magnitude.
Successfully designs an achromatic beam deflector with high efficiency.
Systematically explores near-global optima for robust device performance.
Abstract
HiLAB (Hybrid inverse-design with Latent-space learning, Adjoint-based partial optimizations, and Bayesian optimization) is a new paradigm for inverse design of nanophotonic structures. Combining early-terminated topological optimization (TO) with a Vision Transformer-based variational autoencoder (VAE) and a Bayesian search, HiLAB addresses multi-functional device design by generating diverse freeform configurations at reduced simulation costs. Shortened adjoint-driven TO runs, coupled with randomized physical parameters, produce robust initial structures. These structures are compressed into a compact latent space by the VAE, enabling Bayesian optimization to co-optimize geometry and physical hyperparameters. Crucially, the trained VAE can be reused for alternative objectives or constraints by adjusting only the acquisition function. Compared to conventional TO pipelines prone to…
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