Inductively Scalable, Single-Step Neural Surrogates for Wave-Scattering Inverse Problems
Neural network surrogates are an emerging alternative to traditional electromagnetic wave simulators like finite-difference time-domain (FDTD); their goal is to replace rigorous physical simulations with pre-trained neural networks that solve wave-scattering forward and inverse problems orders of magnitude faster. However, nonrecurrent, single-step surrogates have scaled only to a few tens of simulation variables. Here, we show that this barrier can be overcome by dynamically generating salient training examples during training, rather than randomly sampling the large space of possible examples. We introduce an algorithm that runs in parallel with surrogate training, using gradient ascent...
| Authors | Charles Dove, Laura Waller |
|---|---|
| Primary category | physics.optics |
| Published | 18 August 2026 |
| Last updated at source | 18 August 2026 |
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