3 ms·
The Bitter Lesson only applies when you don't know the function you are modeling (e.g. perfect chess strategy or text-to-speech). On the contrary, neural netwo
by Archit3ch 2y ago
The Bitter Lesson only applies when you don't know the function you are modeling (e.g. perfect chess strategy or text-to-speech).
On the contrary, neural networks will never give you a better way to convert to the frequency domain than the Fourier Transform. At best, they might approximate it.
- Matthyze 2y agoRight. And the use-case for PINNs is, e.g., modeling a known function that is not solvable analytically?
- constantcrying 2y ago>And the use-case for PINNs is, e.g., modeling a known function that is not solvable analytically? No. That is the use case for numerical analysis, where you can develop a high performance, accurate algorithm based on the mathematical analysis of the problem. It is really kind of silly to want to encode the solution in some neural network.
- Matthyze 2y agoSo, what's the use-case for PINNs, then?
- constantcrying 2y agoAllegedly you can use it to solve PDEs, although that does not seem to work well. Maybe there are some very special problems, without well developed silver where they work.