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Thanks for the pointer to Fourier optics! I mentioned Fourier because it is something I still don't "get" even after studying and using these methods. I am sti

Thanks for the pointer to Fourier optics! I mentioned Fourier because it is something I still don't "get" even after studying and using these methods. I am still learning, but I have so far only applied inverse methods to systems where the physics is well-defined and the model already accepted as "real". For example, CT scanner algorithms which model the photon counters and attenuation through matter (although this can get complicated...), with the output being data in well-defined format (like the distribution of matter). I do see people using inverse solvers or AI to derive something which isn't an "image" but a model of the physics (does that make sense?). The extreme version of this which I have seen once before, is a paper using AI to extract algebraic forms of PDEs from videos of fluids. To what extent does the AI user have to understand/"pre-model" the physics, and to what extent does the AI generate understanding in some way? Apologies if this is misinformed as I am still outside the research community, and don't very actively read papers. Currently I am trying to learn background to understand electromagnetic properties of materials with an interest to apply this to computer graphics. I eventually want to learn what processes are currently done for extracting material properties from scans, etc., along the lines of the classic gonioreflectometers. In this space, from the papers I look at, it looks like AI is unavoidable and I am sure an extremely useful tool but I definitely want to be careful not to get lost in misunderstanding.

dotnet00 3y ago
>To what extent does the AI user have to understand/"pre-model" the physics, and to what extent does the AI generate understanding in some way? The conclusion that I reached is that you need to understand at least enough to be able to handle basic scrutiny from people in the same field. So, for example, if you come up with an AI for deriving some model of the physics of a CT scanner, you should at least understand the problem you're tackling enough to make meaningful comparisons to any popular competing approaches that might exist. That understanding should automatically encompass things like understanding how your variables are bounded/how they behave. A contrived example could be with an AI intended to guess 2 prime factors of a given number. Imagine someone making such a tool without even knowing what a prime number is, where the only benchmark they use for correctness is if its outputs fit the dataset of products of prime numbers given. The tool would be clearly useless, because while to the AI researcher it'd seem fine that it outputs 36, which is close to the intended 37, to anyone who knows about prime numbers it'd be an immediate warning sign needing further elaboration and explanation. The researcher could protest that as an AI expert, they don't know anything about prime numbers, but that doesn't really matter in research. Similarly, due to my non-physics background, while I was (and to an extent still am) given significant leeway in terms of my ability to respond to detailed physics questions about my work, by now I am expected to be able to handle questions about things that are common knowledge among physicists in this field (so, for example, knowing what performing a Fourier transform means in our context, understanding the high level functioning of the key components of the machines we work with, having some intuition for when a simulation result is unrealistic etc). If I were to talk about how I've simulated some of these components, but drew a complete blank when asked how my work improved upon the common knowledge means of simulating them, I'd obviously look like a complete idiot :P It may seem like a pretty low and obvious bar, but surprisingly I have seen many cases of researchers who didn't even care about reaching that level.

dotnet00 3y ago

>To what extent does the AI user have to understand/"pre-model" the physics, and to what extent does the AI generate understanding in some way? The conclusion that I reached is that you need to understand at least enough to be able to handle basic scrutiny from people in the same field. So, for example, if you come up with an AI for deriving some model of the physics of a CT scanner, you should at least understand the problem you're tackling enough to make meaningful comparisons to any popular competing approaches that might exist. That understanding should automatically encompass things like understanding how your variables are bounded/how they behave. A contrived example could be with an AI intended to guess 2 prime factors of a given number. Imagine someone making such a tool without even knowing what a prime number is, where the only benchmark they use for correctness is if its outputs fit the dataset of products of prime numbers given. The tool would be clearly useless, because while to the AI researcher it'd seem fine that it outputs 36, which is close to the intended 37, to anyone who knows about prime numbers it'd be an immediate warning sign needing further elaboration and explanation. The researcher could protest that as an AI expert, they don't know anything about prime numbers, but that doesn't really matter in research. Similarly, due to my non-physics background, while I was (and to an extent still am) given significant leeway in terms of my ability to respond to detailed physics questions about my work, by now I am expected to be able to handle questions about things that are common knowledge among physicists in this field (so, for example, knowing what performing a Fourier transform means in our context, understanding the high level functioning of the key components of the machines we work with, having some intuition for when a simulation result is unrealistic etc). If I were to talk about how I've simulated some of these components, but drew a complete blank when asked how my work improved upon the common knowledge means of simulating them, I'd obviously look like a complete idiot :P It may seem like a pretty low and obvious bar, but surprisingly I have seen many cases of researchers who didn't even care about reaching that level.