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8:30 AM
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Registration
()
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9:10 AM
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Welcome and aims of the workshop
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Nora Brambilla
()
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9:25 AM
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Machine learning is ubiquitous
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Maria-Paola Lombardo
()
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9:55 AM
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Generative flow methods
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Sinead Ryan
(until 10:45 AM)
()
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9:55 AM
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Aspects of scaling and scalability for flow-based samplers
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Daniel Hackett
()
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10:20 AM
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Learning trivializing flows
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David Albandea
()
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10:45 AM
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--- Coffee break ---
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11:15 AM
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Generative flow methods
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Sinead Ryan
(until 12:40 PM)
()
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11:15 AM
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Fourier-Flow model generating Feynman paths
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Lingxiao Wang
()
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11:40 AM
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Stochastic normalizing flows for lattice field theory
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Elia Cellini
()
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12:00 PM
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Panel discussion
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Members: Simone Bacchio
Maria Paola Lombardo
Daniel Hackett
Alberto Ramos
Chair: Sinead Ryan
()
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9:50 AM
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Model-Independent Learning of Quantum Phases of Matter with Quantum Convolutional Neural Networks
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Frank Pollmann
()
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10:20 AM
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Automatic differentiation for Lattice gauge theories
-
Alberto Ramos
()
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10:45 AM
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--- Coffee break ---
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11:15 AM
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Gauge field generation
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Phiala Shanahan
(until 1:00 PM)
()
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11:15 AM
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Simulation of the 2D Schwinger Model via machine-learned flows in Global Correction steps
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Jacob Finkenrath
()
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11:40 AM
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Variational Autoregressive Networks for Information Theory
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Tomasz Stebel
()
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12:00 PM
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Machine-learning-assisted Monte Carlo fails at sampling computationally hard problems
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Jeanne Trinquier
()
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12:20 PM
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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
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Jeanne Trinquier
Jacob Finkenrath
Members: Fernando Romero Lopez
Chair: Phiala Shanahan
()
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8:45 AM
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Introducing Munich Data Science Institute
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Sylvia Kortüm
()
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9:00 AM
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EFT, Lattice and ML
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Nora Brambilla
(Physik Department, TU Munich)
(until 10:40 AM)
()
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9:00 AM
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Learning Trivializing Gradient Flows
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Simone Bacchio
()
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9:40 AM
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An analysis of Bayesian estimates for missing higher orders in perturbative calculations
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Aleksas Mazeliauskas
()
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10:10 AM
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Generative models and EFTs
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Marina Marinkovic
()
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10:40 AM
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--- Coffee break ---
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11:10 AM
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EFT, Lattice and ML
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Nora Brambilla
(Physik Department, TU Munich)
(until 1:00 PM)
()
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11:10 AM
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Finite-volume pionless EFT for multi-nucleon systems with differential programming
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Fernando Romero-Lopez
()
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11:40 AM
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Panel discussion: 1. What can EFTs do for ML4LATTICE? 2. What ML4LATTICE could do for EFTs?
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William Detmold
Aleksas Mazeliauskas
Phiala Shanahan
Chair: Nora Brambilla
Members: Marina Marinkovic
()
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9:30 AM
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ML for particle physics
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Lukas Heinrich
()
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10:00 AM
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Complex Langevin real-time simulations and ML
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Alexander Rothkopf
()
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10:30 AM
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ML for physical interpretation of lattice results
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Alexander Rothkopf
(until 10:50 AM)
()
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10:30 AM
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Towards fully bayesian analyses in Lattice QCD
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Julien Frison
()
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10:50 AM
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--- Coffee Break ---
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11:15 AM
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ML for physical interpretation of lattice results
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Alexander Rothkopf
(until 1:00 PM)
()
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11:15 AM
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Physical Concepts from Neural Networks with Two Inputs
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Sebastian Wetzel
()
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11:35 AM
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Ab-Initio Quantum Chemistry via Graph Neural Networks
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Nicholas Gao
()
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12:00 PM
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Panel discussion on ML for physics interpretation
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Miles Cranmer
Members: Sebastian Wetzel
Matteo Favoni
Fernando Romero Lopez
Chair: Alexander Rothkopf
()
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9:00 AM
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ML and quantum field theories
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Gert Aarts
()
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9:30 AM
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Inverse Problem
(until 11:00 AM)
()
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9:30 AM
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Machine learning hadron spectral functions in Lattice QCD
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Gabor Papp
()
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10:00 AM
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R-ratio from Lattice QCD
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Jian Liang
()
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10:30 AM
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Inverse problem solving in nuclear physics with deep learning
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Kai Zhou
()
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11:00 AM
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--- Coffee break ---
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11:30 AM
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Inverse Problem
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Andreas Kronfeld
(until 12:30 PM)
()
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11:30 AM
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On the extraction of hadronic spectral densities from Euclidean correlators
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Alessandro De Santis
()
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12:00 PM
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Panel discussion
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Chair: Andreas Kronfeld
()
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1:00 PM
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--- Lunch ---
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2:30 PM
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ML for particle physics
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Lukas Heinrich
(until 4:00 PM)
()
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2:30 PM
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Discovering mathematical structures with neural networks
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Sven Krippendorf
()
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2:50 PM
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Machine Learning at IceCube
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Rasmus Orsoe
()
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3:10 PM
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Improving Normalizing Flows to Sample from Boltzmann Distributions
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Vincent Stimper
()
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3:30 PM
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Panel discussion:
-
Chair: Lukas Heinrich
()
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4:00 PM
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--- Coffee break ---
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4:30 PM
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ML for phase transitions and sign problem mitigation
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Will Detmold
(until 6:00 PM)
()
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4:30 PM
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Signal-to-noise improvement with contour deformations
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Gurtej Kanwar
()
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4:50 PM
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Complex normalizing flows and subtractions for sign problems
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Yukari Yamauchi
()
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5:10 PM
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Applying Complex Valued Neural Networks to the Hubbard Model Sign Problem: A Survey and Case Study
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Marcel Rodekamp
()
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5:30 PM
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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
-
Andrei Alexandru
(The George Washington University)
Members: Gurtej Kanwar
Yukari Yamauchi
Chair: Will Detmold
Thomas Luu
()
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1:00 PM
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--- Group Photo ---
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1:15 PM
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--- Lunch ---
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2:30 PM
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ML for phase transitions and sign problem mitigation
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Maria-Paola Lombardo
(until 4:00 PM)
()
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2:30 PM
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Machine learning for quantum field theories with a sign problem
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Andrei Alexandru
()
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2:50 PM
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A machine learning approach to the classification of phase transitions in many flavor QCD
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Marius Neumann
()
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3:10 PM
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Teaching how to teach: defining learning samples to detect phase transitions
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Francesco Di Renzo
()
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3:30 PM
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Panel discussion: 1. Competitivity of ML for Phase identification 2. ML for Hypothesised Phases 3. Potential of ML for sign porblem
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Chair: Maria Paola Lombardo
()
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4:00 PM
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--- Coffee break ---
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4:30 PM
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Spectral Reconstruction
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Gert Aarts
(until 6:00 PM)
()
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4:30 PM
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Brief introduction on spectral functions and their computation
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Gert Aarts
()
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4:40 PM
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Spectral reconstruction with Gaussian processes
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Julian Urban
()
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5:10 PM
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Spectral reconstruction with neural networks
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Lingxiao Wang
Kai Zhou
()
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5:40 PM
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Learning regulators for spectral reconstruction
-
Alexander Rothkopf
()
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5:50 PM
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Panel discussion
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Chair: Gert Aarts
()
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1:00 PM
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--- Lunch ---
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2:30 PM
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ML as component of exact algorithms
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Phiala Shanahan
(until 4:35 PM)
()
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2:30 PM
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Building Transport Maps and Making Good Use of Them
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Michael Albergo
()
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3:00 PM
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Automatic differentiation for Lattice QCD
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A. Ramos
()
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3:25 PM
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Symbolic Distillation of Neural Networks
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Miles Cranmer
()
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3:55 PM
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Panel discussion: 1. Meaning of "exact" algorithms 2. in-principle vs in-practice exactness 3. applications for exactness
-
Miles Cranmer
Members: Michael Albergo
Andrei Alexandru
Chair: Phiala Shanahan
()
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4:35 PM
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--- Reception ---
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6:00 PM
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How to accelerate gauge field field generation using flow-based and hybrid models
-
Phiala Shanahan
()
|
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1:00 PM
|
--- Lunch ---
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2:30 PM
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Symmetry equivariant neural networks/informed neural networks
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Phiala Shanahan
(until 3:40 PM)
()
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2:30 PM
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Generation of gauge field configurations with equivariant neural networks
-
Matteo Favoni
()
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2:50 PM
|
TBA
-
Daniel Hackett
()
|
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3:05 PM
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Panel discussion: 1. Symmetry-equivariance vs computational brute force 2. Computational challenges of gauge equivariance
-
Members: Gurtej Kanwar
Matteo Favoni
Christoph Lehner
Chair: Phiala Shanahan
()
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3:40 PM
|
Poster session (With food and drinks)
(until 6:40 PM)
()
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12:30 PM
|
--- Lunch ---
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2:00 PM
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EFT, Lattice and ML
(until 3:15 PM)
()
|
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2:00 PM
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Machine learning for symbolic computation in high energy physics
-
Abdulhakim Alnuqayqdan
()
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2:25 PM
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Alleviating the Sign Problem via Contour Deformation and Machine Learning
-
Thomas Luu
()
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2:50 PM
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Gauge-equivariant neural networks as preconditioners in lattice QCD
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Christoph Lehner
()
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3:15 PM
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--- Coffee break ---
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3:40 PM
|
EFT, Lattice and ML
(until 5:00 PM)
()
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3:40 PM
|
Deep Learning and the Standard Model: a philosophy of science perspective
-
Luigi Scorzato
()
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4:10 PM
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The route towards phase transition recognition in Lattice Gauge Theories
-
Andreas Athenodorou
()
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4:40 PM
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Search for Exotic Higgs Bosons using Quantum Machine Learning
-
Nakul Aggarwal
()
|
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5:00 PM
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Summary
-
Nora Brambilla
()
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