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ASC school 2022: Physics meets Artificial Intelligence

Sep 12 – 16, 2022
ASC
Europe/Berlin timezone

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Contribution List

66 out of 66 displayed
  1. 1. Generative Models, Manifolds and Symmetries: Tools
    Danilo Rezende (Deepmind)
    9/12/22, 9:00 AM
  2. 2. Simulation-based inference for physics and beyond
    Alex Cole (Amsterdam University)
    9/12/22, 11:00 AM
  3. 3. Generative models and many-body physics
    Roger Melko (Perimeter Institute, Waterloo)
    9/12/22, 2:30 PM
  4. 4. Understanding your network - towards interpretability in machine learning applications in many-body physics
    Annabelle Bohrdt (Harvard University)
    9/12/22, 4:30 PM
  5. 5. Generative Models, Manifolds and Symmetries: Applications
    Danilo Rezende (DeepMind)
    9/13/22, 9:00 AM
  6. 6. Autoregressive models and quantum state reconstruction
    Roger Melko (Perimeter Institute, Waterloo)
    9/13/22, 11:00 AM
  7. 7. Mathematical Foundations of Deep Learning
    Gitta Kutyniok (LMU Munich)
    9/13/22, 2:30 PM
  8. 8. Machine learning for string theory
    Alex Cole (Amsterdam University)
    9/13/22, 4:30 PM
  9. 9. Tutorial: Quantum state reconstruction of Rydberg atom arrays
    Roger Melko (Perimeter Institute, Waterloo)
    9/13/22, 4:30 PM
  10. 10. Postersession
    9/13/22, 6:00 PM
  11. 11. Artificial Intelligence for Precision Cosmology
    Daniel Grün (LMU Munich)
    9/14/22, 9:00 AM
  12. 12. Beyond ground states: Assisting sampling of physical states with generative models
    Marylou Gabrie (École Polytechnique, Paris)
    9/14/22, 11:00 AM
  13. 13. Reliable AI: Dream or Reality?
    Gitta Kutyniok (LMU Munich)
    9/14/22, 2:30 PM
  14. 14. Beyond ground states: Assisting sampling of physical states with generative models
    Marylou Gabrie (École Polytechnique, Paris)
    9/15/22, 9:00 AM
  15. 15. Neural network quantum states: from ground states to dynamics
    Giuseppe Carleo (EPFL, Lausanne)
    9/15/22, 11:00 AM
  16. 16. ML and LHC Physics
    Tilman Plehn (Heidelberg University)
    9/15/22, 2:30 PM
  17. 18. Boosting event generation & unfolding with generative networks
    Theo Heimel (Heidelberg University), Tilman Plehn (Heidelberg University)
    9/15/22, 4:30 PM
  18. 17. Neural network quantum states: from ground states to dynamics
    Giuseppe Carleo (EPFL, Lausanne)
    9/15/22, 4:30 PM
  19. 19. Postersession
    9/15/22, 6:00 PM
  20. 20. Bayesian Networks and Uncertainties
    Tilman Plehn (Heidelberg University)
    9/16/22, 9:00 AM
  21. 21. Boosting event generation & unfolding with generative networks
    Theo Heimel (Heidelberg University), Tilman Plehn (Heidelberg University)
    9/16/22, 11:00 AM
  22. 22. NetKetl: hands-on tutorial on neural network quantum states (Tutorial)
    Filippo Vicentini (EPFL, Lausanne)
    9/16/22, 11:00 AM
  23. 49. Adaptive Quantum State Tomography with Active Learning
    Hannah Lange (LMU Munich)
  24. 61. Anomaly Detection in Mass Spectrometry Data of Water Samples
    Viktoria Maike Paula Pauw (LMU, LRZ)
  25. 29. Antifragile Persistent Homology using Fisher Information
    Karthik Viswanathan (University of Amsterdam)
  26. 50. Bias-free evaluation of small data sets using machine-learning methods
    Steffen Steinert (Chair of Physics Education, LMU Munich; TU Kaiserslautern)
  27. 26. Bidirectional optimal quantum control boosted by deep learning: A use case of polarization in liquid crystals
    Dominik Vašinka (Palacký University Olomouc)
  28. 68. Bloqade! An open playground for quantum simulation.
    Anna Knörr (Perimeter Institute for Theoretical Physics, QuEra Computing Inc.), Ejaaz Merali (University of Waterloo)
  29. 62. Bubble theory: a new parametrization for classifications problems
    Barbara Soda (Perimeter Institute for Theoretical Physics and University of Waterloo)
  30. 34. Can Artificial Intelligence Improve the Simulation of Realtime Dynamics of Quantum Fields in NISQ Era?
    Chinonso Onah (LMU Munich)
  31. 52. Can linear regression predict parameters for measuring a disordered quantum device?
    Mr Sathish R Kuppuswamy (Delft university of Technology)
  32. 41. Classifying Anomalies THrough Outer Density Estimation (CATHODE)
    Manuel Sommerhalder (University of Hamburg)
  33. 69. CO2 emissions from physics research?
    Anna Knörr (Perimeter Institute for Theoretical Physics)
  34. 32. Deep Learning for the Modeling and Inverse Design of Radiative Heat Transfer
    Juan José García-Esteban (Universidad Autónoma de Madrid)
  35. 47. Design of a deep neural network suitable for real-time feedback strategies discovered via reinforcement learning on a quantum device
    Jonas Landgraf (Max Planck Institute for the Science of Light)
  36. 60. Equivariance in Smoothed Particle Hydrodynamics
    Mr Artur Toshev (TUM)
  37. 51. Field-resolved molecular fingerprinting of human blood for disease detection
    Mr Philip Jacob (Max Planck Institute of Quantum Optics, Garching, Germany)
  38. 39. How Machine Learning Helps to Find Efficient Organic Photocatalysts for Hydrogen Generation
    Viola Steidl
  39. 48. Identification of mature thunderstorms in NWP simulations using artificial neural networks
    Kianusch Vahid Yousefnia (German Aerospace Center (DLR), Institute for Atmospheric Physics, Oberpfaffenhofen, Germany)
  40. 67. IEA-GAN: Intra-Event Aware GAN with Relational Reasoning for the Fast Detector Simulation
    Mr Hosein Hashemi (LMU Munich)
  41. 59. Individualized AI-ePortfolios for the entry phase in STEM degree programs (KI4TUK)
    Stefan Küchemann (LMU Munich, Faculty of Physics, Chair of Physics Education)
  42. 43. Inferring the assembly and merger histories of galaxies with the IllustrisTNG simulations and machine learning
    Lukas Eisert (Max Planck Institute for Astronomy)
  43. 53. Limits and prospects of molecular fingerprinting revealed through in silico and predictive modeling
    Tarek Eissa (Faculty of Physics, Ludwig Maximilian University Munich)
  44. 66. MLOps for High Energy Physics
    Daniel Holmberg (University of Helsinki)
  45. 37. Operator Inference - a non-intrusive reduced-order modeling framework for plasma simulations
    Constantin Gahr
  46. 35. Optimizing the classification of biological tissues using polarized data supported by Machine Learning
    Carla Rodríguez (Optics Group, Physics Department, Universitat Autònoma de Barcelona, Barcelona)
  47. 27. Physics solutions for machine learning privacy leaks
    José Ramón Pareja Monturiol (Instituto de Ciencias Matemáticas (ICMAT))
  48. 42. Predicting the Thermal Sunyaev-Zel'dovich Field using Modular and Equivariant Set-Based Neural Networks
    Leander Thiele (Princeton Univ)
  49. 55. Random Network Distillation for Optimal Training Data Selection
    Samuel Tovey (University of Stuttgart)
  50. 31. Relative entropy in neural networks
    Yanick Thurn (Julius-Maximilians-University of Wuerzburg)
  51. 33. Replacing neural networks by optimal analytical predictors for the detection of phase transitions
    Julian Arnold (University of Basel)
  52. 58. Representing neural activity using quantum generative adversarial networks
    Vinicius Fonseca Hernandes (Delft University of Technology)
  53. 30. Reservoir Computing with Many-Body Simulations
    Mr Mario U. Gaimann (Stuttgart Center for Simulation Science (SimTech), Cluster of Excellence EXC 2075, University of Stuttgart)
  54. 65. Snaphot-based analysis of quantum many-body systems with variational Monte Carlo and Neural Networks
    Christian Manfred Reinmoser
  55. 44. Spectroscopic evidence for engineered hadron formation in repulsive fermionic SU(N) Hubbard Models
    Miklós Antal Werner (Budapest University of Technology and Economics)
  56. 45. Stable fusion turbulence simulations through hybrid numerical-AI methods
    Robin Greif (Max-Planck Institute for Plasma Physics)
  57. 54. Statistical Dynamics and Linearity of Weakly Correlated Gradient Descent Based Learning
    Ori Shem-Ur
  58. 64. SwarmRL: Active Colloids meet Reinforcement Learning
    David Zimmer
  59. 40. Symmetries, Safety, and Self-Supervision
    Lorenz Vogel (Heidelberg University)
  60. 36. Symmetry-protected Bose-Einstein condensation of interacting hardcore bosons
    Reja Helene Wilke (Department of Physics, Arnold Sommerfeld Center for Theoretical Physics, University of Munich, Germany)
  61. 63. Towards interpretability of ANNs for spectroscopic data: inductive bias, lottery tickets, and input optimization
    Jakub Vrabel (CEITEC, Brno University of Technology)
  62. 56. Towards using Reinforcement Learning to navigate the String Flux Landscape
    Mr Abhishek Dubey (Ludwig Maximilians Universität)
  63. 28. Uncertainty Quantification for Multiscale Turbulent Transport Simulations of a Magnetic Confinement Fusion Device using Machine Learning Surrogate Models
    Yehor Yudin (Max Planck Institute for Plasma Physics)
  64. 46. Unraveling Quantum Scrambling with Neural Networks
    Jan Olle (Max Planck Institute for the Science of Light)
  65. 70. Unsupervised Interpretable Learning of Phases From Many-Qubit Systems
    Nico Sadoune
  66. 38. Validation of quasilinear transport models in the ASTRA framework
    Michael Bergmann (IPP)