ASC school 2022: Physics meets Artificial Intelligence

Beitragsliste

1. Generative Models, Manifolds and Symmetries: Tools

Danilo Rezende (Deepmind)

12.09.22, 09:00

2. Simulation-based inference for physics and beyond

Alex Cole (Amsterdam University)

12.09.22, 11:00

3. Generative models and many-body physics

Roger Melko (Perimeter Institute, Waterloo)

12.09.22, 14:30

4. Understanding your network - towards interpretability in machine learning applications in many-body physics

Annabelle Bohrdt (Harvard University)

12.09.22, 16:30

5. Generative Models, Manifolds and Symmetries: Applications

Danilo Rezende (DeepMind)

13.09.22, 09:00

6. Autoregressive models and quantum state reconstruction

Roger Melko (Perimeter Institute, Waterloo)

13.09.22, 11:00

7. Mathematical Foundations of Deep Learning

Gitta Kutyniok (LMU Munich)

13.09.22, 14:30

8. Machine learning for string theory

Alex Cole (Amsterdam University)

13.09.22, 16:30

9. Tutorial: Quantum state reconstruction of Rydberg atom arrays

Roger Melko (Perimeter Institute, Waterloo)

13.09.22, 16:30

10. Postersession

13.09.22, 18:00

11. Artificial Intelligence for Precision Cosmology

Daniel Grün (LMU Munich)

14.09.22, 09:00

12. Beyond ground states: Assisting sampling of physical states with generative models

Marylou Gabrie (École Polytechnique, Paris)

14.09.22, 11:00

13. Reliable AI: Dream or Reality?

Gitta Kutyniok (LMU Munich)

14.09.22, 14:30

14. Beyond ground states: Assisting sampling of physical states with generative models

Marylou Gabrie (École Polytechnique, Paris)

15.09.22, 09:00

15. Neural network quantum states: from ground states to dynamics

Giuseppe Carleo (EPFL, Lausanne)

15.09.22, 11:00

16. ML and LHC Physics

Tilman Plehn (Heidelberg University)

15.09.22, 14:30

18. Boosting event generation & unfolding with generative networks

Theo Heimel (Heidelberg University), Tilman Plehn (Heidelberg University)

15.09.22, 16:30

17. Neural network quantum states: from ground states to dynamics

Giuseppe Carleo (EPFL, Lausanne)

15.09.22, 16:30

19. Postersession

15.09.22, 18:00

20. Bayesian Networks and Uncertainties

Tilman Plehn (Heidelberg University)

16.09.22, 09:00

21. Boosting event generation & unfolding with generative networks

Theo Heimel (Heidelberg University), Tilman Plehn (Heidelberg University)

16.09.22, 11:00

22. NetKetl: hands-on tutorial on neural network quantum states (Tutorial)

Filippo Vicentini (EPFL, Lausanne)

16.09.22, 11:00

49. Adaptive Quantum State Tomography with Active Learning

Hannah Lange (LMU Munich)

61. Anomaly Detection in Mass Spectrometry Data of Water Samples

Viktoria Maike Paula Pauw (LMU, LRZ)

29. Antifragile Persistent Homology using Fisher Information

Karthik Viswanathan (University of Amsterdam)

50. Bias-free evaluation of small data sets using machine-learning methods

Steffen Steinert (Chair of Physics Education, LMU Munich; TU Kaiserslautern)

26. Bidirectional optimal quantum control boosted by deep learning: A use case of polarization in liquid crystals

Dominik Vašinka (Palacký University Olomouc)

68. Bloqade! An open playground for quantum simulation.

Anna Knörr (Perimeter Institute for Theoretical Physics, QuEra Computing Inc.), Ejaaz Merali (University of Waterloo)

62. Bubble theory: a new parametrization for classifications problems

Barbara Soda (Perimeter Institute for Theoretical Physics and University of Waterloo)

34. Can Artificial Intelligence Improve the Simulation of Realtime Dynamics of Quantum Fields in NISQ Era?

Chinonso Onah (LMU Munich)

52. Can linear regression predict parameters for measuring a disordered quantum device?

Herr Sathish R Kuppuswamy (Delft university of Technology)

41. Classifying Anomalies THrough Outer Density Estimation (CATHODE)

Manuel Sommerhalder (University of Hamburg)

69. CO2 emissions from physics research?

Anna Knörr (Perimeter Institute for Theoretical Physics)

32. Deep Learning for the Modeling and Inverse Design of Radiative Heat Transfer

Juan José García-Esteban (Universidad Autónoma de Madrid)

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)

60. Equivariance in Smoothed Particle Hydrodynamics

Herr Artur Toshev (TUM)

51. Field-resolved molecular fingerprinting of human blood for disease detection

Herr Philip Jacob (Max Planck Institute of Quantum Optics, Garching, Germany)

39. How Machine Learning Helps to Find Efficient Organic Photocatalysts for Hydrogen Generation

Viola Steidl

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)

67. IEA-GAN: Intra-Event Aware GAN with Relational Reasoning for the Fast Detector Simulation

Herr Hosein Hashemi (LMU Munich)

59. Individualized AI-ePortfolios for the entry phase in STEM degree programs (KI4TUK)

Stefan Küchemann (LMU Munich, Faculty of Physics, Chair of Physics Education)

43. Inferring the assembly and merger histories of galaxies with the IllustrisTNG simulations and machine learning

Lukas Eisert (Max Planck Institute for Astronomy)

53. Limits and prospects of molecular fingerprinting revealed through in silico and predictive modeling

Tarek Eissa (Faculty of Physics, Ludwig Maximilian University Munich)

66. MLOps for High Energy Physics

Daniel Holmberg (University of Helsinki)

37. Operator Inference - a non-intrusive reduced-order modeling framework for plasma simulations

Constantin Gahr

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)

27. Physics solutions for machine learning privacy leaks

José Ramón Pareja Monturiol (Instituto de Ciencias Matemáticas (ICMAT))

42. Predicting the Thermal Sunyaev-Zel'dovich Field using Modular and Equivariant Set-Based Neural Networks

Leander Thiele (Princeton Univ)

55. Random Network Distillation for Optimal Training Data Selection

Samuel Tovey (University of Stuttgart)

31. Relative entropy in neural networks

Yanick Thurn (Julius-Maximilians-University of Wuerzburg)

33. Replacing neural networks by optimal analytical predictors for the detection of phase transitions

Julian Arnold (University of Basel)

58. Representing neural activity using quantum generative adversarial networks

Vinicius Fonseca Hernandes (Delft University of Technology)

30. Reservoir Computing with Many-Body Simulations

Herr Mario U. Gaimann (Stuttgart Center for Simulation Science (SimTech), Cluster of Excellence EXC 2075, University of Stuttgart)

65. Snaphot-based analysis of quantum many-body systems with variational Monte Carlo and Neural Networks

Christian Manfred Reinmoser

44. Spectroscopic evidence for engineered hadron formation in repulsive fermionic SU(N) Hubbard Models

Miklós Antal Werner (Budapest University of Technology and Economics)

45. Stable fusion turbulence simulations through hybrid numerical-AI methods

Robin Greif (Max-Planck Institute for Plasma Physics)

54. Statistical Dynamics and Linearity of Weakly Correlated Gradient Descent Based Learning

Ori Shem-Ur

64. SwarmRL: Active Colloids meet Reinforcement Learning

David Zimmer

40. Symmetries, Safety, and Self-Supervision

Lorenz Vogel (Heidelberg University)

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)

63. Towards interpretability of ANNs for spectroscopic data: inductive bias, lottery tickets, and input optimization

Jakub Vrabel (CEITEC, Brno University of Technology)

56. Towards using Reinforcement Learning to navigate the String Flux Landscape

Herr Abhishek Dubey (Ludwig Maximilians Universität)

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)

46. Unraveling Quantum Scrambling with Neural Networks

Jan Olle (Max Planck Institute for the Science of Light)

70. Unsupervised Interpretable Learning of Phases From Many-Qubit Systems

Nico Sadoune

38. Validation of quasilinear transport models in the ASTRA framework

Michael Bergmann (IPP)