ML surrogate example #482
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ML surrogate example #482
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RTSandberg
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ax3l
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ax3l
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Dec 12, 2023
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| Sigmoid = 4 | ||
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| def get_enum_type(type_to_test, EnumClass): |
Check notice
Code scanning / CodeQL
Explicit returns mixed with implicit (fall through) returns Note
| def download_and_unzip(url, data_dir): | ||
| request.urlretrieve(url, data_dir) | ||
| with tarfile.open(data_dir) as tar_dataset: | ||
| tar_dataset.extractall() |
Check failure
Code scanning / CodeQL
Arbitrary file write during tarfile extraction High
ax3l
reviewed
Dec 13, 2023
ax3l
reviewed
Dec 13, 2023
ax3l
reviewed
Dec 13, 2023
ax3l
reviewed
Dec 13, 2023
ax3l
reviewed
Dec 13, 2023
ax3l
reviewed
Dec 13, 2023
ax3l
reviewed
Dec 13, 2023
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An example using PyTorch neural network surrogates in ImpactX. The neural networks are trained on data from WarpX simulations.
This is an example of 9 stages of Laser-plasma accelerator elements coupled with simple 3 cm beam transport between stages using idealized plasma lenses as illustrated in the below schematic.
Some initial phase space projections are as shown:
At the end of the 9 stages, the beam evolves to this:
The beam moments evolve as shown:
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