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A comment on JAX – NumPy on the CPU, GPU, and TPU, with great automatic differentiation. Parent comment

Re:JAX – NumPy on the CPU, GPU, and TPU, with great automati... by ssivark on Friday September 29, 2023 @12:31PM (#37706613)
> In my "physics of biomedical imaging" bubble, people are hardly doing state-of-the-art ML, but rather expensive forward models for which computing a gradient is cumbersome.

I’d appreciate any pointers to the literature; curious to see the kinds of models people work with. Thanks!

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Re:JAX – NumPy on the CPU, GPU, and TPU, with great automati... by cl3misch on Friday September 29, 2023 @01:08PM (#37707171)
I don't have didactic examples at hand, but e.g. [1] or [2]. IIRC [1] uses the Laplace operator (second-order spatial derivate) and [2] uses a linear solve inside the forward model through which differentiation is certainly possible but pretty cumbersome in practice.

[1] https://www.nature.com/articles/s41598-019-52283-6

[2] https://doi.org/10.1117/1.JMI.4.3.034005

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