Neural networks with automatic differentiation can solve complex inverse design problems more efficiently than traditional methods, especially when handling constraints and discontinuous inputs.
This paper uses neural networks to solve an inverse optics problem: designing 2D reflectors that bend light from an extended source into a desired far-field pattern. The authors propose two differentiable loss functions and compare their neural approach against a traditional deconvolution baseline, showing faster convergence and better accuracy across multiple test cases.