Neural operators are deep learning architectures that approximate nonlinear operators, for instance, to learn the solution to a parametric PDE. The DeepONet is one type in which we can query the output at arbitrary points. Here is the code: github.com/Ceyron/machine-lea...
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Timestamps:
00:00 Intro
01:03 What are Neural Operators?
01:58 DeepONet does not return full output field
02:31 About learning 1d antiderivative operator
04:33 Unstacked DeepONets
07:15 JAX-related notes (using vmap)
11:33 Imports
12:35 Download and inspect the dataset
21:30 Implementing DeepONet architecture
32:54 Training loop
41:20 Qualitative evaluation
45:35 Test error metric
50:40 Outro