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

Monday, February 27
- 1
  
  Registration
- 2
  
  Welcome and aims of the workshop
  
  Speaker: Nora Brambilla
- 3
  
  Machine learning is ubiquitous
  
  Speaker: Maria-Paola Lombardo
  
  Munich_Feb23_MpL.pdf
- Generative flow methods
  
  Talks and panel discussion
  
  Convener: Sinead Ryan
  - 4
    
    Aspects of scaling and scalability for flow-based samplers
    
    Speaker: Daniel Hackett
    
    hackett-tum-talk.pdf
  - 5
    
    Learning trivializing flows
    
    Speaker: David Albandea
    
    David_Munich-ML-approaches.pdf
- 10:45 AM
  
  Coffee break
- Generative flow methods
  
  Talks and panel discussion
  
  Convener: Sinead Ryan
  - 6
    
    Fourier-Flow model generating Feynman paths
    
    Speaker: Lingxiao Wang
    
    FFM_Munich0227_lingxiao.pdf
  - 7
    
    Stochastic normalizing flows for lattice field theory
    
    Speaker: Elia Cellini
    
    EliaCellini_SNFLFT_ML_approaches.pdf
  - 8
    
    Panel discussion
    
    Speakers: Chair: Sinead Ryan, Members: Simone Bacchio, Daniel Hackett, Maria Paola Lombardo, Alberto Ramos
    
    Panel.pdf
- 1:00 PM
  
  Lunch
- ML for particle physics
  
  Talks and panel discussion
  
  Convener: Lukas Heinrich
  - 9
    
    Discovering mathematical structures with neural networks
    
    Speaker: Sven Krippendorf
    
    lattice-workshop.pdf
  - 10
    
    Machine Learning at IceCube
    
    Speaker: Rasmus Orsoe
    
    GNNs in IceCube (Lattice QCD).pdf
  - 11
    
    Improving Normalizing Flows to Sample from Boltzmann Distributions
    
    Speaker: Vincent Stimper
    
    FAB_ML4Lattice_Workshop_Feb_2023.pdf
  - 12
    
    Panel discussion:
    
    Speaker: Chair: Lukas Heinrich
    
    PanelDiscussion.pdf
- 4:00 PM
  
  Coffee break
- ML for phase transitions and sign problem mitigation: Contour deformation methods for noise reduction
  
  Talks and panel discussion
  
  Convener: Will Detmold
  - 13
    
    Signal-to-noise improvement with contour deformations
    
    Speaker: Gurtej Kanwar
    
    ContourDeforms_StN_27_02_2023_PDF.pdf
  - 14
    
    Complex normalizing flows and subtractions for sign problems Zoom
    
    Zoom
    
    https://tum-conf.zoom.us/j/68490931000 Meeting-ID: 684 9093 1000 Passcode: ML4Lattice
    
    Speaker: Yukari Yamauchi
    
    YYamauchi.pdf
  - 15
    
    Applying Complex Valued Neural Networks to the Hubbard Model Sign Problem: A Survey and Case Study
    
    Speaker: Marcel Rodekamp
    
    main.pdf
  - 16
    
    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
    
    1) Is there a way to determine the maximum improvement in <sign> or in S-to-N that is achievable through contour deformations and related
    approaches?
    
    2) How can ML be targeted towards such a maximal improvement?
    
    3) Are there features of specific observables that make contour
    deformation particularly effective or ineffective?
    
    4) How do contour deformations for one observable/theory translate to other observables/theories?
    
    Speakers: Chair: Will Detmold, Members: Gurtej Kanwar, Andrei Alexandru (The George Washington University), Thomas Luu, Yukari Yamauchi
    
    2023_Munich_ML4Lattice.pdf
Tuesday, February 28
- 17
  
  Model-Independent Learning of Quantum Phases of Matter with Quantum Convolutional Neural Networks
  
  Speaker: Frank Pollmann
  
  Pollmann_IAS_QCNN.pdf
- 18
  
  Automatic differentiation for Lattice gauge theories
  
  Speaker: Alberto Ramos
  
  ADML.pdf
- 10:45 AM
  
  Coffee break
- Gauge field generation
  
  Talks and panel discussion
  
  Convener: Phiala Shanahan
  - 19
    
    Simulation of the 2D Schwinger Model via machine-learned flows in Global Correction steps
    
    Speaker: Jacob Finkenrath
    
    FlowGC-ML4QCD.pdf
  - 20
    
    Variational Autoregressive Networks for Information Theory Zoom
    
    Zoom
    
    https://tum-conf.zoom.us/j/68490931000 Meeting-ID: 684 9093 1000 Passcode: ML4Lattice
    
    Speaker: Tomasz Stebel
    
    stebel_ML_QCD.pdf
  - 21
    
    Machine-learning-assisted Monte Carlo fails at sampling computationally hard problems
    
    Speaker: Jeanne Trinquier
    
    SAMPLING_PRESENTATION.pdf
  - 22
    
    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
    
    • Temperature check: for each panelist, in 2-5 words, how optimistic or pessimistic do you feel about the utility of ML in accelerating lattice field generation in your context.
    • What are the most application-specific features of the LQCD gauge field generation problem i.e., in what important ways is it different from applications in other areas of physics, and how does that impact the way field generation algorithms must be designed?
    • What are the biggest challenges to lattice field generation accelerated by ML?
    • Circling back to the start: what are your reasons for optimism or pessimism in reaching applications at an interesting scale?
    
    Speakers: Chair: Phiala Shanahan, Members: Fernando Romero Lopez, Jacob Finkenrath, Jeanne Trinquier
    
    04_Panel_2023_Munich_ML4Lattice_configgenPanel.pdf
- 1:00 PM
  
  Group Photo
- 1:15 PM
  
  Lunch
- ML for phase transitions and sign problem mitigation
  
  Talks and panel discussion
  
  Convener: Maria-Paola Lombardo
  - 23
    
    Machine learning for quantum field theories with a sign problem
    
    Speaker: Andrei Alexandru
    
    alexandru-ml-latticeqcd-workshop.pdf
  - 24
    
    A machine learning approach to the classification of phase transitions in many flavor QCD
    
    Speaker: Marius Neumann
    
    Neumann_ML4Lattice.pdf
  - 25
    
    Teaching how to teach: defining learning samples to detect phase transitions
    
    Speaker: Francesco Di Renzo
    
    FDR_28feb2023_TUM.pdf
  - 26
    
    Panel discussion: 1. Competitivity of ML for Phase identification 2. ML for Hypothesised Phases 3. Potential of ML for sign porblem
    
    Panel discussion addressing the three main questions:
    
    --Where do we stand with the identification of phases with ML?
    Do you feel ML could become competitive with standard techniques for the quantitative studies of known phase transitions i.e. order of the transition at large Nf, chiral limit, etc.
    
    --Which are your views on the need/concrete possibility of identifying hypothesised, not yet detected phase transitions - in particular confinement, axial restoration, anomalous threshold in the QGP
    
    --Can you summarise the status of the sign problem (without ML) (finite baryon density/real time) and how do you assess the potentiality of ML to mitigate it?
    
    Speaker: Chair: Maria Paola Lombardo
    
    ML_PTsign.pdf
- 4:00 PM
  
  Coffee break
- Spectral Reconstruction
  
  Convener: Gert Aarts
  - 27
    
    Brief introduction on spectral functions and their computation
    
    Speaker: Gert Aarts
    
    talkmunichML2023.pdf
  - 28
    
    Spectral reconstruction with Gaussian processes
    
    Speaker: Julian Urban
    
    MLQCD-2023-Urban.pdf
  - 29
    
    Spectral reconstruction with neural networks
    
    Speakers: Kai Zhou, Lingxiao Wang
    
    SF_Munich0228_kai_lingxiao.pdf
  - 30
    
    Learning regulators for spectral reconstruction
    
    Speaker: Alexander Rothkopf
    
    Round_Table_SpectralFunc_EXPORT.pdf
  - 31
    
    Panel discussion
    
    Speaker: Chair: Gert Aarts
Wednesday, March 1
- 32
  
  Introducing Munich Data Science Institute
  
  Speaker: Sylvia Kortüm
- EFT, Lattice and ML
  
  Talks and panel discussion
  
  Convener: Nora Brambilla (Physik Department, TU Munich)
  - 33
    
    Learning Trivializing Gradient Flows
    
    Speaker: Simone Bacchio
    
    Bacchio_gflow.pdf
  - 34
    
    An analysis of Bayesian estimates for missing higher orders in perturbative calculations
    
    Speaker: Aleksas Mazeliauskas
    
    mazeliauskas_TUM.pdf
  - 35
    
    Generative models and EFTs
    
    Speaker: Marina Marinkovic
    
    23-03-01_EFTs_for_ML_v3.pdf
- 10:40 AM
  
  Coffee break
- EFT, Lattice and ML
  
  Talks and panel discussion
  
  Convener: Nora Brambilla (Physik Department, TU Munich)
  - 36
    
    Finite-volume pionless EFT for multi-nucleon systems with differential programming
    
    Speaker: Fernando Romero-Lopez
    
    munich.pdf
  - 37
    
    Panel discussion: 1. What can EFTs do for ML4LATTICE? 2. What ML4LATTICE could do for EFTs?
    
    --what can EFTs do for ML4LATTICE?
    --what ML4LATTICE could do for EFTs?
    
    Speakers: Chair: Nora Brambilla, Members: Marina Marinkovic, Phiala Shanahan, William Detmold, Aleksas Mazeliauskas
    
    panelMLEFT2023.pdf
- 1:00 PM
  
  Lunch
- ML as component of exact algorithms
  
  Talks and panel discussion
  
  Convener: Phiala Shanahan
  - 38
    
    Building Transport Maps and Making Good Use of Them
    
    Speaker: Michael Albergo
    
    munich_mar2023.pptx
  - 39
    
    Automatic differentiation for Lattice QCD
    
    Speaker: A. Ramos
    
    ADML2.pdf
  - 40
    
    Symbolic Distillation of Neural Networks Zoom
    
    Zoom
    
    https://tum-conf.zoom.us/j/68490931000 Meeting-ID: 684 9093 1000 Passcode: ML4Lattice
    
    Speaker: Miles Cranmer
    
    interpretable_ml__mar_2023_export.pdf
  - 41
    
    Panel discussion: 1. Meaning of "exact" algorithms 2. in-principle vs in-practice exactness 3. applications for exactness
    
    • What does it mean to have an ML-accelerated algorithm that is “exact”?
    Discuss the distinction between exact algs, interpretable algs, and algs that allow error propagation.
    • What are the differences between in-principle and in-practice exactness? Are they important?
    • In what applications (both in LQFT and drawing parallels to other ares in physics) is it important to guarantee exactness in ML-accelerated algorithms, and where is it unnecessary, impossible, or worth sacrificing?
    
    Speakers: Chair: Phiala Shanahan, Members: Michael Albergo, Andrei Alexandru, Miles Cranmer
    
    2023_Munich_ML4Lattice_exactalgPanel.pdf
- 4:35 PM
  
  Reception
- 42
  
  How to accelerate gauge field field generation using flow-based and hybrid models
  
  Speaker: Phiala Shanahan
Thursday, March 2
- 43
  
  ML for particle physics
  
  Speaker: Lukas Heinrich
- 44
  
  Complex Langevin real-time simulations and ML
  
  Speaker: Alexander Rothkopf
  
  Complex_Langevin_Kernels_EXPORT.pdf
  
  Complex_Langevin_Kernels_EXPORT.pptx
- ML for physical interpretation of lattice results
  
  Talks and panel discussion
  
  Convener: Alexander Rothkopf
  - 45
    
    Towards fully bayesian analyses in Lattice QCD
    
    Speaker: Julien Frison
    
    Presentation.pdf
- 10:50 AM
  
  Coffee Break
- ML for physical interpretation of lattice results
  
  Talks and panel discussion
  
  Convener: Alexander Rothkopf
  - 46
    
    Physical Concepts from Neural Networks with Two Inputs
    
    Speaker: Sebastian Wetzel
    
    2023MunichWetzel.pdf
  - 47
    
    Ab-Initio Quantum Chemistry via Graph Neural Networks
    
    Speaker: Nicholas Gao
    
    ml4lattice.pptx
  - 48
    Panel discussion on ML for physics interpretation
    
    Alexander Rothkopf: Setting the stage – ML for physics interpretation
    
    Sebastian Wetzel: ML for identifying order parameters & effective d.o.f. in the context of phase transitions
    
    Matteo Favoni: ML for identifying defects and effective d.o.f. in the context of confinement
    
    Fernando Romero Lopez: ML for extraction of emergent d.o.f. in the context of scattering resonance
    
    Miles Cranmer: ML for inference of governing equations in the context of orbital mechanics (remote)
    
    Discussion with the audience
    
    Speakers: Chair: Alexander Rothkopf, Members: Sebastian Wetzel, Matteo Favoni, Fernando Romero Lopez, Miles Cranmer
    
    2023MunichInterpretationPanel.pdf
    
    Defects_and_center_vortices.pdf
    
    panel_frl.pdf
    
    Round_Table_ML_PhysicsInterpretation_v2.pdf
- 1:00 PM
  
  Lunch
- Symmetry equivariant neural networks/informed neural networks
  
  Talks and panel discussion
  
  Convener: Phiala Shanahan
  - 49
    
    Generation of gauge field configurations with equivariant neural networks
    
    Speaker: Matteo Favoni
    
    Generation_of_gauge_field_configurations_with_equivariant_neural_networks__Munich_.pdf
  - 50
    
    TBA
    
    Speaker: Daniel Hackett
    
    hackett-tum-talk-2.pdf
  - 51
    
    Panel discussion: 1. Symmetry-equivariance vs computational brute force 2. Computational challenges of gauge equivariance
    
    • There is ongoing debate in the ML community about the value of trying to incorporate domain knowledge into architectures vs using computational brute force. What guides your intuition for the value of building symmetry-equivariant architectures? In your experience when is symmetry-equivariance valuable and when is it not?
    • What makes gauge equivariance in particular challenging from a computational efficiency perspective?
    
    Speakers: Chair: Phiala Shanahan, Members: Gurtej Kanwar, Christoph Lehner, Matteo Favoni
    
    2023_Munich_ML4Lattice_gaugeequivPanel.pdf
- Poster session (With food and drinks)
Friday, March 3
- 52
  
  ML and quantum field theories
  
  Speaker: Gert Aarts
  
  talkmunichML2023-QFTML.pdf
- Inverse Problem
  
  Talks and panel discussion
  - 53
    
    Machine learning hadron spectral functions in Lattice QCD
    
    Speaker: Gabor Papp
    
    München_20230303_1.pptx
  - 54
    
    R-ratio from Lattice QCD
    
    Speaker: Jian Liang
  - 55
    
    Inverse problem solving in nuclear physics with deep learning
    
    Speaker: Kai Zhou
    
    inverse_problem_KaiZhou.pdf
- 11:00 AM
  
  Coffee break
- Inverse Problem
  
  Talks and panel discussion
  
  Convener: Andreas Kronfeld
  - 56
    
    On the extraction of hadronic spectral densities from Euclidean correlators
    
    Speaker: Alessandro De Santis
    
    talk.pdf
  - 57
    
    Panel discussion
    
    Speaker: Chair: Andreas Kronfeld
    
    ml4l-inverse-panel.pdf
- 12:30 PM
  
  Lunch
- EFT, Lattice and ML
  
  Talks and panel discussion
  - 58
    
    Machine learning for symbolic computation in high energy physics zoom
    
    zoom
    
    https://tum-conf.zoom.us/j/68490931000 Meeting-ID: 684 9093 1000 Passcode: ML4Lattice
    
    Speaker: Abdulhakim Alnuqayqdan
  - 59
    
    Alleviating the Sign Problem via Contour Deformation and Machine Learning
    
    Speaker: Thomas Luu
    
    Luu_ML4LQCD_2023.pdf
  - 60
    
    Gauge-equivariant neural networks as preconditioners in lattice QCD
    
    Speaker: Christoph Lehner
    
    talk.pdf
- 3:15 PM
  
  Coffee break
- EFT, Lattice and ML
  
  Talks and panel discussion
  - 61
    
    Deep Learning and the Standard Model: a philosophy of science perspective
    
    Speaker: Luigi Scorzato
    
    Deep Learning and the Standard Model - a philosophy of science point of view.pdf
  - 62
    
    The route towards phase transition recognition in Lattice Gauge Theories
    
    Speaker: Andreas Athenodorou
  - 63
    
    Search for Exotic Higgs Bosons using Quantum Machine Learning
    
    Speaker: Nakul Aggarwal
- 64
  
  Summary
  
  Speaker: Nora Brambilla

Machine Learning approaches in Lattice QCD - An interdisciplinary exchange

Institute for Advanced Study of the Technische Universität München

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