AI4NP Winter School

Jan 11, 2021, 8:00 AM → Jan 15, 2021, 6:00 PM US/Eastern

Virtual

Amber Boehnlein (JLAB), Paulo Bedaque (University of Maryland), Tanja Horn (Catholic University of America)

Artificial Intelligence (AI) is a rapidly developing field focused on computational technologies that can be trained, with data, to augment or automate human skill. A subset of AI is machine learning (ML), which is usually grouped into supervised, unsupervised and reinforcement learning. Nuclear Physics is big data: the gigantic data volumes produced in modern experiments now and over the next decade are reaching scales and complexities that require computational methods for tasks such as big data analytics, design of new detectors, controls, and calibration systems. AI has the potential to provide the methodologies to optimize operating parameters and perform theoretical calculations of nuclear many-body systems. The AI4NP Winter School will give the participants a deeper understanding on what Artificial Intelligence and Machine Learning are and how they can be used to analyze nuclear physics data, design new detectors, controls, and calibration systems for nuclear physics experiments and perform theoretical calculations of nuclear many-body systems. The AI4NP lecture topics will emphasize active Nuclear Physics research, both experiment and theory, that relies on AI/ML techniques, as well as synergies between the computer science and the NP communities and inspire areas for possible collaboration in order to foster vital contributions to urgent and long-term challenges for nuclear physics.

Monday, January 11

Day 1

1 Neural networks and deep learning

Speaker: Michelle Kuchera (Davidson College)

GitHub materials

2 Convolutional Neural Networks

Speaker: Michelle Kuchera (Davidson College)

GitHub materials

1:00 PM Break

3 Autoencoders -> Latent Spaces

Speaker: Michelle Kuchera (Davidson College)

GitHub materials

4 Autoencoders, Generative Adversarial Networks -> Generative models

Speaker: Michelle Kuchera (Davidson College)

GitHub materials

Tuesday, January 12

Day 2

5 Variational Monte Carlo and Machine Learning

Speakers: Dr Alessandro Lovato (Argonne National Laboratory), Corey Adams (Argonne National Laboratory)

AI4NP_School: Variational Monte Carlo

6 Detector design optimization-1

Speaker: Dr Cristiano Fanelli (MIT)

AI4NP: Detector Optimization

1:00 PM Break

7 Detector Design Optimization-2

Speaker: Dr Cristiano Fanelli (MIT)

AI4NP: Detector Optimization

8 Detector Optimization-3

Speaker: Dr Cristiano Fanelli (MIT)

AI4NP: Detector Optimization

Wednesday, January 13

Day 3

9 Machine Learning and Global Analysis

Speaker: Nobuo Sato (ODU)

Ai4NP-ML and QCD global analysis.pdf

10 Variational Monte Carlo and Machine Learning 2

Speakers: Dr Alessandro Lovato (Argonne National Laboratory), Corey Adams (Argonne National Laboratory)

AI4NP_School: Variational Monte Carlo

1:00 PM Break

11 Variational Monte Carlo and Machine Learning-3-4

Speakers: Dr Alessandro Lovato (Argonne National Laboratory), Corey Adams (Argonne National Laboratory)

AI4NP_School: Variational Monte Carlo

AI4NP_School.zip

Thursday, January 14

Day 4

12 Detector design optimization-4

Speaker: Dr Cristiano Fanelli (MIT)

AI4NP: Detector Optimization

13 Machine Learning and Global Analysis 2

Speaker: Nobuo Sato (ODU)

1:00 PM Break

14 Machine Learning and Global Analysis 3-4

Speaker: Nobuo Sato (ODU)

Friday, January 15

Day 5

15 Neural Networks and Deep Learning Q&A

Speaker: Michelle Kuchera (Davidson College)

16 Variational Monte Carlo and Machine Learning Q&A

Speakers: Dr Alessandro Lovato (Argonne National Laboratory), Corey Adams (Argonne National Laboratory)

17 Detector design optimization Q&A

Speaker: Dr Cristiano Fanelli (MIT)

18 Data set feature extraction Q&A

Speaker: Nobuo Sato (ODU)