Webinaires

ML-enhancement of simulation and optimization in electromagnetism

Annonce

Résumé

Full-wave simulations of large-scale electromagnetic devices—spanning thousands of wavelengths while featuring sub-wavelength geometrical details—pose significant computational challenges. These simulations are critical in the design and optimization of metamaterials, where resolving fine-scale features is essential to predict macroscopic behavior accurately. In this talk, we present a specific use case where surrogate models are used to accelerate full-wave simulations of optical metasurfaces, thereby enabling iterative optimization loops that would otherwise be prohibitively expensive. We present scientific machine learning approaches for training these surrogate models to increase both the speed of evaluation and the data efficiency. Beyond surrogate modeling, we also demonstrate how machine learning can enhance optimization tasks independently of the underlying solver. In particular, we highlight methods for learning representations of the design space and guiding the search toward optimal configurations, thus improving the overall efficiency of the design process.