Machine learning for lattice field theory and beyond

Speaker List

• Evan Berkowitz (Forschungszentrum Jülich)
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    Learning about the Hubbard Model

• Piotr Bialas (Jagiellonian University)
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    Gradient estimators without action derivative in Schwinger model.

• Elia Cellini (University of Turin/ INFN Turin)
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    Normalizing Flows for Effective String Theory

• Aurélien Decelle (Universidad Complutense de Madrid)
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    The Restricted Boltzman Machine: Phase Diagram, Generation and Interpretability

• Matteo Favoni (TU Vienna)
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    Using equivariant neural networks as maps of gauge field configurations

• Jorge FERNANDEZ DE COSSIO DIAZ (ENS PARIS)
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    Disentangling representations in Restricted Boltzmann Machines without adversaries

• Mathis Gerdes (University of Amsterdam)
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    Continuous flows and transfer learning

• Ouraman Hajizadeh
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    Training a Gomoku-Agent using DRL

• Kieran Holland (University of the Pacific)
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    Machine learning a fixed point action

• Andreas Ipp (TU Wien)
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    Visualizing the inner workings of L-CNNs

• Gurtej Kanwar (University of Bern)
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    Mitigating signal-to-noise problems using learned contour deformations

• Javad Komijani (ETH Zurich)
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    EFT-inspired generative models for simulations of quantum field theories

• Anindita Maiti (Harvard University)
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    $λφ^4$ Scalar Neural Network Field Theory

• Anindita Maiti (Harvard University)
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    $λφ^4$ Scalar Neural Network Field Theory

• Nobuyuki Matsumoto (RIKEN BNL)
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    Trivializing map as a coarse-graining map

• Alessandro Nada (Università degli Studi di Torino)
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    Stochastic normalizing flows as out-of-equilibrium transformations

• Alfonso Navas Gomez (Complutense University of Madrid)
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    Inferring effective couplings with Restricted Boltzmann Machines

• Kim Nicoli (University of Bonn - HISKP)
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    Machine Learned Thermodynamics of Physical Systems Across Critical Phases

• Misaki Ozawa (CNRS, Univ. Grenoble Alpes, France)
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    Renormalization Group Approach for Machine Learning Hamiltonian

• Chan Ju Park (Swansea University)
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    Scalar field Restricted Boltzmann Machine as an ultraviolet regulator

• Alexander Rothkopf (University of Stavanger)
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    Machine Learning assisted real-time simulations with Complex Langevin

• Pietro Rotondo (University of Parma)
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    Statistical mechanics of deep learning beyond the infinite-width limit

• Daniel Schuh (TU Wien)
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    Global and local symmetries in neural networks

• Daniel Spitz (Institute for Theoretical Physics, Heidelberg University)
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    Interpretable order parameters from persistent homology in non-Abelian lattice gauge theory

• Julian Urban (Institute for Theoretical Physics Heidelberg)
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    Instantaneous gauge field generation with approximate trivializing maps

• Lorenz Vaitl (TU Berlin)
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    Path gradient estimators for CNFs in Lattice Gauge Theory

• Roberto Verdel (ICTP)
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    Data-driven discovery of relevant information in many-body problems: from spin lattice models to quantum field simulators

• Roberto Verdel Aranda (The Abdus Salam International Centre for Theoretical Physics (ICTP))
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    Data-driven discovery of relevant information in many-body problems: from spin lattice models to quantum field simulators

• Neill Warrington (Institute for Nuclear Theory)
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    Thimbology and Qubits

• Urs Wenger
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    Machine learning a fixed point action

• Tilo Wettig (University of Regensburg)
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    Gauge-equivariant multigrid neural networks

• Yukari Yamauchi (The Institute for Nuclear Theory)
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    Deforming complex-valued distributions via machine learning

• Kai Zhou (Frankfurt Institute for Advanced Studies)
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    Deep Learning Inverse Problems in Extreme QCD Matter Study