7 papers
Neural Operator-enabled Topology-informed Evolutionary Strategy for PDE-Constrained Optimization
Xiangming Huang, Guannan Zhang, Lu Lu +2
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. Ge…
VLM-Aware Meta-Optic Front-End Design for Frozen Vision-Language Models
Chanik Kang, Raphaël Pestourie, Haejun Chung
Conventional machine-vision pipelines typically rely on high-quality optics that produce clean, human-interpretable images, and optical design has therefore been driven by image-le…
Interpretable Meta-Learning for Multi-Objective Chemical Search
Antonio Varagnolo, Yulia Pimonova, Michael G. Taylor +2
Navigating the vast space of synthetically accessible molecules demands surrogate models that are interpretable and capable of handling multiple competing objectives at the same ti…
Physics Enhanced Deep Surrogates for the Phonon Boltzmann Transport Equation
Antonio Varagnolo, Giuseppe Romano, Raphaël Pestourie
Designing materials with controlled heat flow at the nano-scale is central to advances in microelectronics, thermoelectrics, and energy-conversion technologies. At these scales, ph…
Inverse Design in Nanophotonics via Representation Learning
Reza Marzban, Ali Adibi, Raphael Pestourie
Inverse design in nanophotonics, the computational discovery of structures achieving targeted electromagnetic (EM) responses, has become a key tool for recent optical advances. Tra…
HiLAB: A Hybrid Inverse-Design Framework
Reza Marzban, Hamed Abiri, Raphael Pestourie +1
HiLAB (Hybrid inverse-design with Latent-space learning, Adjoint-based partial optimizations, and Bayesian optimization) is a new paradigm for inverse design of nanophotonic struct…