View Code? Open in Web Editor
NEW
Surrogate Beamline -- training NN to emulate Shadow simulations of Xray beamlines
Jupyter Notebook 94.92%
Python 5.08%
surrogatebl's Introduction
- beamline_29.py
- shadow implmentation fo 29ID (don't recall which branch)
- torchTools.py
- homebrew tools to hasten NN build/training/testing
- IEX_100k_04w.pkl
- Pickle of Shadow3 simulated data from IEX_OO_Shadow_tests.ipynb
- IEX_100k_04w_scaling.pkl
- Pickle of feature scaling used in IEX_surrogate_NN.ipynb
- IEX_100k_04w_NN_results.pkl
- Pickle of models and results from IEX_surrogate_NN.ipynb
- IEX_OO_Shadowtests.ipynb
- Testing out beamline_29.py
- playing with ranges of each DOF
- build data set for surrogate
- IEX_surrogate_NN.ipynb
- train and test NN on shadow generated data
- IEX_shadow_vs_surrogate.ipynb
- test time usage of both beamlines
- GA_vs_beamline.ipynb
- in progress
- goal to test out simple genetic algorithm to find optimal beamline configuration
- Gaussian process implementation (variation of MG-GPO)
- Bluesky version of beamline
- Either adapt beamline_29.py or surrogate model
- Construct plan that can implement optimization technqiue
surrogatebl's People
Contributors
Watchers