Name: Machine Learning approaches in Lattice QCD - An interdisciplinary exchange
Start: 2023-02-27T08:00:00+01:00
End: 2023-03-03T20:00:00+01:00
Location: Institute for Advanced Study of the Technische Universität München

Machine Learning approaches in Lattice QCD - An interdisciplinary exchange

from Monday, February 27, 2023 (8:00 AM) to Friday, March 3, 2023 (8:00 PM)

Monday, February 27, 2023

8:30 AM Registration
Registration
8:30 AM - 9:10 AM
9:10 AM Welcome and aims of the workshop - Nora Brambilla
Welcome and aims of the workshop
- Nora Brambilla
9:10 AM - 9:25 AM
9:25 AM Machine learning is ubiquitous - Maria-Paola Lombardo
Machine learning is ubiquitous
- Maria-Paola Lombardo
9:25 AM - 9:55 AM
9:55 AM
9:55 AM - 10:45 AM
Contributions

9:55 AM Aspects of scaling and scalability for flow-based samplers - Daniel Hackett

10:20 AM Learning trivializing flows - David Albandea
10:45 AM Coffee break
Coffee break
10:45 AM - 11:15 AM
11:15 AM
11:15 AM - 12:40 PM
Contributions

11:15 AM Fourier-Flow model generating Feynman paths - Lingxiao Wang

11:40 AM Stochastic normalizing flows for lattice field theory - Elia Cellini

12:00 PM Panel discussion - Chair: Sinead Ryan Members: Simone Bacchio Daniel Hackett Maria Paola Lombardo Alberto Ramos
1:00 PM Lunch
Lunch
1:00 PM - 2:30 PM
2:30 PM
2:30 PM - 4:00 PM
Contributions

2:30 PM Discovering mathematical structures with neural networks - Sven Krippendorf

2:50 PM Machine Learning at IceCube - Rasmus Orsoe

3:10 PM Improving Normalizing Flows to Sample from Boltzmann Distributions - Vincent Stimper

3:30 PM Panel discussion: - Chair: Lukas Heinrich
4:00 PM Coffee break
Coffee break
4:00 PM - 4:30 PM
4:30 PM Contour deformation methods for noise reduction
Contour deformation methods for noise reduction
4:30 PM - 6:00 PM
Contributions

4:30 PM Signal-to-noise improvement with contour deformations - Gurtej Kanwar

4:50 PM Complex normalizing flows and subtractions for sign problems - Yukari Yamauchi

5:10 PM Applying Complex Valued Neural Networks to the Hubbard Model Sign Problem: A Survey and Case Study - Marcel Rodekamp

5:30 PM Panel discussion: 1. maximum improvement in <sign> 2. ML towards such a maximal improvement 3. Contour deformation for specific observables 4. Translation between observables/theories - Chair: Will Detmold Members: Gurtej Kanwar Andrei Alexandru (The George Washington University) Thomas Luu Yukari Yamauchi
Tuesday, February 28, 2023
9:50 AM Model-Independent Learning of Quantum Phases of Matter with Quantum Convolutional Neural Networks - Frank Pollmann
Model-Independent Learning of Quantum Phases of Matter with Quantum Convolutional Neural Networks
- Frank Pollmann
9:50 AM - 10:20 AM
10:20 AM Automatic differentiation for Lattice gauge theories - Alberto Ramos
Automatic differentiation for Lattice gauge theories
- Alberto Ramos
10:20 AM - 10:45 AM
10:45 AM Coffee break
Coffee break
10:45 AM - 11:15 AM
11:15 AM
11:15 AM - 1:00 PM
Contributions

11:15 AM Simulation of the 2D Schwinger Model via machine-learned flows in Global Correction steps - Jacob Finkenrath

11:40 AM Variational Autoregressive Networks for Information Theory - Tomasz Stebel

12:00 PM Machine-learning-assisted Monte Carlo fails at sampling computationally hard problems - Jeanne Trinquier

12:20 PM Panel discussion: 1. Utility of ML for lattice 2. application-specific features of gauge field generation 3. Biggest challenges 4. Applications at an interesting scale - Chair: Phiala Shanahan Members: Fernando Romero Lopez Jacob Finkenrath Jeanne Trinquier
1:00 PM Group Photo
Group Photo
1:00 PM - 1:15 PM

1:15 PM Lunch
Lunch
1:15 PM - 2:30 PM
2:30 PM
2:30 PM - 4:00 PM
Contributions

2:30 PM Machine learning for quantum field theories with a sign problem - Andrei Alexandru

2:50 PM A machine learning approach to the classification of phase transitions in many flavor QCD - Marius Neumann

3:10 PM Teaching how to teach: defining learning samples to detect phase transitions - Francesco Di Renzo

3:30 PM Panel discussion: 1. Competitivity of ML for Phase identification 2. ML for Hypothesised Phases 3. Potential of ML for sign porblem - Chair: Maria Paola Lombardo
4:00 PM Coffee break
Coffee break
4:00 PM - 4:30 PM
4:30 PM
4:30 PM - 6:00 PM
Contributions

4:30 PM Brief introduction on spectral functions and their computation - Gert Aarts

4:40 PM Spectral reconstruction with Gaussian processes - Julian Urban

5:10 PM Spectral reconstruction with neural networks - Kai Zhou Lingxiao Wang

5:40 PM Learning regulators for spectral reconstruction - Alexander Rothkopf

5:50 PM Panel discussion - Chair: Gert Aarts
Wednesday, March 1, 2023
8:45 AM Introducing Munich Data Science Institute - Sylvia Kortüm
Introducing Munich Data Science Institute
- Sylvia Kortüm
8:45 AM - 9:00 AM
9:00 AM
9:00 AM - 10:40 AM
Contributions

9:00 AM Learning Trivializing Gradient Flows - Simone Bacchio

9:40 AM An analysis of Bayesian estimates for missing higher orders in perturbative calculations - Aleksas Mazeliauskas

10:10 AM Generative models and EFTs - Marina Marinkovic
10:40 AM Coffee break
Coffee break
10:40 AM - 11:10 AM
11:10 AM
11:10 AM - 1:00 PM
Contributions

11:10 AM Finite-volume pionless EFT for multi-nucleon systems with differential programming - Fernando Romero-Lopez

11:40 AM Panel discussion: 1. What can EFTs do for ML4LATTICE? 2. What ML4LATTICE could do for EFTs? - Chair: Nora Brambilla Members: Marina Marinkovic Phiala Shanahan William Detmold Aleksas Mazeliauskas
1:00 PM Lunch
Lunch
1:00 PM - 2:30 PM
2:30 PM
2:30 PM - 4:35 PM
Contributions

2:30 PM Building Transport Maps and Making Good Use of Them - Michael Albergo

3:00 PM Automatic differentiation for Lattice QCD - A. Ramos

3:25 PM Symbolic Distillation of Neural Networks - Miles Cranmer

3:55 PM Panel discussion: 1. Meaning of "exact" algorithms 2. in-principle vs in-practice exactness 3. applications for exactness - Chair: Phiala Shanahan Members: Michael Albergo Andrei Alexandru Miles Cranmer
4:35 PM Reception
Reception
4:35 PM - 6:00 PM
6:00 PM How to accelerate gauge field field generation using flow-based and hybrid models - Phiala Shanahan
How to accelerate gauge field field generation using flow-based and hybrid models
- Phiala Shanahan
6:00 PM - 7:00 PM
Thursday, March 2, 2023
9:30 AM ML for particle physics - Lukas Heinrich
ML for particle physics
- Lukas Heinrich
9:30 AM - 10:00 AM
10:00 AM Complex Langevin real-time simulations and ML - Alexander Rothkopf
Complex Langevin real-time simulations and ML
- Alexander Rothkopf
10:00 AM - 10:30 AM
10:30 AM
10:30 AM - 10:50 AM
Contributions

10:30 AM Towards fully bayesian analyses in Lattice QCD - Julien Frison
10:50 AM Coffee Break
Coffee Break
10:50 AM - 11:15 AM
11:15 AM
11:15 AM - 1:00 PM
Contributions

11:15 AM Physical Concepts from Neural Networks with Two Inputs - Sebastian Wetzel

11:35 AM Ab-Initio Quantum Chemistry via Graph Neural Networks - Nicholas Gao

12:00 PM Panel discussion on ML for physics interpretation - Chair: Alexander Rothkopf Members: Sebastian Wetzel Matteo Favoni Fernando Romero Lopez Miles Cranmer
1:00 PM Lunch
Lunch
1:00 PM - 2:30 PM
2:30 PM
2:30 PM - 3:40 PM
Contributions

2:30 PM Generation of gauge field configurations with equivariant neural networks - Matteo Favoni

2:50 PM TBA - Daniel Hackett

3:05 PM Panel discussion: 1. Symmetry-equivariance vs computational brute force 2. Computational challenges of gauge equivariance - Chair: Phiala Shanahan Members: Gurtej Kanwar Christoph Lehner Matteo Favoni
3:40 PM

3:40 PM - 6:40 PM
Friday, March 3, 2023
9:00 AM ML and quantum field theories - Gert Aarts
ML and quantum field theories
- Gert Aarts
9:00 AM - 9:30 AM
9:30 AM
9:30 AM - 11:00 AM
Contributions

9:30 AM Machine learning hadron spectral functions in Lattice QCD - Gabor Papp

10:00 AM R-ratio from Lattice QCD - Jian Liang

10:30 AM Inverse problem solving in nuclear physics with deep learning - Kai Zhou
11:00 AM Coffee break
Coffee break
11:00 AM - 11:30 AM
11:30 AM
11:30 AM - 12:30 PM
Contributions

11:30 AM On the extraction of hadronic spectral densities from Euclidean correlators - Alessandro De Santis

12:00 PM Panel discussion - Chair: Andreas Kronfeld
12:30 PM Lunch
Lunch
12:30 PM - 2:00 PM
2:00 PM
2:00 PM - 3:15 PM
Contributions

2:00 PM Machine learning for symbolic computation in high energy physics - Abdulhakim Alnuqayqdan

2:25 PM Alleviating the Sign Problem via Contour Deformation and Machine Learning - Thomas Luu

2:50 PM Gauge-equivariant neural networks as preconditioners in lattice QCD - Christoph Lehner
3:15 PM Coffee break
Coffee break
3:15 PM - 3:40 PM
3:40 PM
3:40 PM - 5:00 PM
Contributions

3:40 PM Deep Learning and the Standard Model: a philosophy of science perspective - Luigi Scorzato

4:10 PM The route towards phase transition recognition in Lattice Gauge Theories - Andreas Athenodorou

4:40 PM Search for Exotic Higgs Bosons using Quantum Machine Learning - Nakul Aggarwal
5:00 PM Summary - Nora Brambilla
Summary
- Nora Brambilla
5:00 PM - 5:15 PM