> So running 500 sequential kMC simulations would take approximately 125 years - not ideal.
Ah I see. Ya it’s hard to get around this…
> is fundamentally stochastic, and not differentiable
This is actually a common “inverse design pattern” for a variety of applications, and luckily there are tricks to efficiently compute gradients here. In my domain (nanophotonics) we’re often simulating incoherent sources, which are similarly stochastic. But there are ways to reformulate the problem to drastically reduce the number of forward and adjoint solves you need during the design process [1].
That being said, given the cost of the forward problem (3 months per iteration!) this doesn’t help you much…
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!
> They require significantly different fabrication processes, and we don't know how to fab them into the same chip as electrical ones.
There are actually a few commercial fabs that will monolithically integrate the photonics, analog electronics, and digital electronics, all in the same CMOS process. See for example GF’s process:
Integrating good optical sources in silicon remains a challenge, but companies like Intel have mastered hybrid bonding and other packaging techniques. TSMC too has a strong silicon photonics effort.
I wonder why ORNL used AMDGPU.jl directly rather than something like KernelAbstractions.jl, which doesn’t require you to overspecialize to a particular architecture. I realize Frontier is all AMD. But the DOE labs have flipped back and forth between HPC platforms a few times. (Which is partly why Sandia invested so heavily in developing Kokkos).
> So running 500 sequential kMC simulations would take approximately 125 years - not ideal.
Ah I see. Ya it’s hard to get around this…
> is fundamentally stochastic, and not differentiable
This is actually a common “inverse design pattern” for a variety of applications, and luckily there are tricks to efficiently compute gradients here. In my domain (nanophotonics) we’re often simulating incoherent sources, which are similarly stochastic. But there are ways to reformulate the problem to drastically reduce the number of forward and adjoint solves you need during the design process [1].
That being said, given the cost of the forward problem (3 months per iteration!) this doesn’t help you much…
[1] https://doi.org/10.1007/s00158-022-03389-5