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Machine Learning approaches in Lattice QCD - An interdisciplinary exchange

→ Europe/Berlin
Institute for Advanced Study of the Technische Universität München

Institute for Advanced Study of the Technische Universität München

Lichtenbergstraße 2a 85748 Garching Germany
Julian Frederic Mayer-Steudte (TUM School of Natural Sciences - T30f), Nora Brambilla (Physik Department, TU Munich), Tom Magorsch (TU Munich), Zeno Kordov (TUM), Antonio Vairo
Description

A workshop for an interdisciplinary exchange for accelerating developments of machine learning techniques in Lattice QCD with experts in Machine learning, Lattice QCD, and other related fields.

This is a Covid safe workshop: FFP2 masks will be required.
Please remember to bring with you FFP2 masks

The conference is broadcasted in zoom if you want to join, contact the organizers via email.

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  • Mon, 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
    • 10:45 AM
      Coffee break
    • Generative flow methods

      Talks and panel discussion

      Convener: Sinead Ryan
    • 1:00 PM
      Lunch
    • ML for particle physics

      Talks and panel discussion

      Convener: Lukas Heinrich
    • 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
  • Tue, 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
  • Wed, 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)
    • 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
  • Thu, 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
        1. Alexander Rothkopf: Setting the stage – ML for physics interpretation
        2. Sebastian Wetzel: ML for identifying order parameters & effective d.o.f. in the context of phase transitions
        3. Matteo Favoni: ML for identifying defects and effective d.o.f. in the context of confinement
        4. Fernando Romero Lopez: ML for extraction of emergent d.o.f. in the context of scattering resonance
        5. Miles Cranmer: ML for inference of governing equations in the context of orbital mechanics (remote)
        6. 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)
  • Fri, 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

    • 64
      Summary
      Speaker: Nora Brambilla
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