Scaling Up Wave Calculations with a Scattering Network

Abstract

Wave scattering is a challenging numerical problem, yet it is central to fields as diverse as seismology, fluid dynamics, acoustics, and photonics. Complex structures scatter waves in random yet deterministic ways. Advances in our understanding and control of scattering are key to applications such as deep-tissue microscopy. However, computing the internal fields on a scale relevant to microscopy remains excessively demanding for both conventional methods and physics-based neural networks. Here, we show how coherent scattering calculations can be scaled up to 21 × 10 6 cubic wavelengths by mapping the physics of multiple scattering onto a deterministic neural network that efficiently harnesses publicly available machine learning infrastructure. We refer to this as a scattering network. Memory usage, an important bottleneck to scaling beyond (10 μm)³, is kept to a minimum by the recurrent network topology and the convolutional derivatives it embodies. Tight integration with an open-source electromagnetic solver enables any researcher with an internet connection to compute complex light-wave scattering throughout volumes as large as (130 μm)³ or 25 mm 2 .