Comment by alechammond

Interesting paper, thanks for sharing! Whenever I see physics-based inverse design using ML surrogates, I always ask, “why not optimize the problem directly” (and eg compute the gradient using an adjoint-variable method)? The paper implies that the forward simulation process isn’t differentiable, but is this true? Thanks!