https://www.jlab.org/remote-ai4np-winter-school

The folder "lectures" contains the slides:

• 1st hour: Introduction to Detector Design with Artificial Intelligence, Bayesian Optimization, the EIC dual-RICH example; real-world applications.

  (intro to Exercise 1)

• 2nd hour: Introduction to Multi-Objective Optimization; Evolutionary Optimization and Genetic Algorithms.

• 3rd hour: Pareto Front, NSGA-II, Decision Making; real-world applications.

  (intro to Exercises 2,3)

• 4th hour: Hands-on session: Exercises 1-3 and Discussion of Solutions.

For the exercises we will use colab:

Exercise 1 https://colab.research.google.com/github//cfteach/AI4NP_detector_opt/blob/master/exe1/exe1_BO.ipynb

Solution 1 https://colab.research.google.com/github//cfteach/AI4NP_detector_opt/blob/master/sol1/driver_bo.ipynb

Exercise 2 https://colab.research.google.com/github/cfteach/AI4NP_detector-opt/blob/master/exe2/exe2_moo.ipynb

Solution 2 https://colab.research.google.com/github/cfteach/AI4NP_detector-opt/blob/master/sol2/driver_moo.ipynb

Exercise 3 https://colab.research.google.com/github/cfteach/AI4NP_detector-opt/blob/master/exe3/exe3_moo_3obj.ipynb

Solution 3 https://colab.research.google.com/github/cfteach/AI4NP_detector-opt/blob/master/sol3/driver_moo_3obj.ipynb

Cristiano Fanelli, [email protected]