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Information and Statistics for Nuclear Experiment and Theory workshop (ISNET-9)

May 22 – 26, 2023
Washington University in St. Louis
US/Central timezone

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Session

ISNET

May 23, 2023, 8:45 AM
Crow 201 (Washington University in St. Louis)

Crow 201

Washington University in St. Louis

Conveners

ISNET: Statistical methods for optimization, Bayesian inference, and uncertainty quantification

  • There are no conveners in this block

ISNET: Statistical methods for optimization, Bayesian inference, and uncertainty quantification

  • There are no conveners in this block

ISNET: Statistical methods for optimization, Bayesian inference, and uncertainty quantification

  • There are no conveners in this block

ISNET: Statistical methods for optimization, Bayesian inference, and uncertainty quantification

  • There are no conveners in this block

ISNET: Symposium in Honor of John Clark: Different Aspects of Machine Learning

  • There are no conveners in this block

ISNET: Symposium in Honor of John Clark: Different Aspects of Machine Learning

  • There are no conveners in this block

ISNET: Symposium in Honor of John Clark: Different Aspects of Machine Learning

  • There are no conveners in this block

ISNET: Symposium in Honor of John Clark: Different Aspects of Machine Learning

  • There are no conveners in this block

ISNET: Emulators and Resampling Techniques

  • There are no conveners in this block

ISNET: Emulators and Resampling Techniques

  • There are no conveners in this block

ISNET: Emulators and Resampling Techniques

  • There are no conveners in this block

ISNET: Emulators and Resampling Techniques

  • There are no conveners in this block

ISNET: Advanced Statistics Techniques for Analyzing Experimental Data and for Accelerator design

  • There are no conveners in this block

ISNET: Advanced Statistics Techniques for Analyzing Experimental Data and for Accelerator design

  • There are no conveners in this block

ISNET: Welcome

  • Brad Jolliff (Washington University)

Presentation materials

ISNET_Welcome_5-23-23 (1).pdf
ISNET_Welcome_5-23-23 (1).pptx
  1. 8. Recent tools and developments in Bayesian statistics
    Yuling Yao (Flatiron Institute)
    5/23/23, 9:00 AM
  2. 9. A statistical exploration of CEMP star classification with s-process models
    Andrés Yagüe López (Los Alamos National Lab)
    5/23/23, 9:30 AM
  3. 10. Global fits and Bayesian inference in "Beyond the Standard Model" physics (virtual)
    Anders Kvellestad (University of Oslo)
    5/23/23, 10:00 AM
  4. 14. Applications of novel chiral interactions to quantum Monte Carlo methods and astrophysical data analysis
    Rahul Somasundaram (Syracuse University)
    5/23/23, 11:00 AM
  5. 30. Sequential Bayesian experimental design for calibration of expensive physics models
    Ozge Sürer
    5/23/23, 11:30 AM
  6. 11. History matching for nuclear ab initio calculations
    Christian Forssén (Durham University)
    5/23/23, 12:00 PM
  7. 13. Bayesian probability updates using sampling/importance resampling: applications in nuclear theory
    Weiguang Jiang (Chalmers University)
    5/23/23, 1:30 PM
  8. 42. Hamiltonian Monte Carlo computation in spatial statistics
    Debashis Mondal (WashU)
    5/23/23, 2:00 PM
  9. 38. Bayesian model calibration for nuclear decays with the Skyrme finite-amplitude method
    Tong Li (LLNL)
    5/23/23, 2:30 PM
  10. 12. Accounting for material and experimental variability using a random effects Bayesian inferential framework
    Denielle Ricciardi (Sandia National Laboratories)
    5/23/23, 3:30 PM
  11. 39. Short talk: Bayesian calibration of viscous anisotropic hydrodynamic simulations of heavy-ion collisions
    Dananjaya Liyanage
    5/23/23, 4:00 PM
  12. 40. Short Talk: How Uncertain Am I? Theoretical errors in Bayesian model calibration for EFTs
    Jason Bub
    5/23/23, 4:15 PM
  13. 61. Prof. John W. Clark
    Henric Krawczynski
    5/24/23, 8:55 AM
  14. 15. John Clark: Physicist at the computational frontier (virtual)
    Henrik Bohr
    5/24/23, 9:00 AM
  15. 16. Machine learning for nuclear physics
    Witek Nazarewicz (MSU/FRIB)
    5/24/23, 9:45 AM
  16. 60. Machine learning of nuclear properties
: a brief tribute to Prof. John Walter Clark (recording)
    Eirene Mavrommatis
    5/24/23, 11:00 AM
  17. 47. Surrogate models of nuclear density functional theory with gaussian processes and autoencoders
    Marc Verriere
    5/24/23, 11:15 AM
  18. 45. Machine learning for heavy-ion accelerators (virtual)
    Yue Hao (Michigan State University)
    5/24/23, 11:45 AM
  19. 17. Nuclear masses learned from a probabilistic neural network
    Amy Lovell (Los Alamos National Lab)
    5/24/23, 1:15 PM
  20. 18. Machine learning for the many-body problem
    Alessandro Lovato (Argonne National laboratory)
    5/24/23, 1:45 PM
  21. 19. Mapping out the thermodynamic stability of a QCD EOS with a critical point using active learning
    Debora Mroczek (University of Illinois)
    5/24/23, 2:15 PM
  22. 46. Predicting nuclear masses with product-unit networks (virtual)
    Babette Dellen
    5/24/23, 3:15 PM
  23. 25. Machine learning for Deeply Virtual Compton Scattering (virtual)
    Manal Almaeen
    5/24/23, 3:45 PM
  24. 23. Short Talk: Deep learning pairing correlations from neural-network quantum states
    Jane Kim
    5/24/23, 4:15 PM
  25. 26. Overview of emulators for nuclear physics
    Dick Furnstahl
    5/25/23, 9:00 AM
  26. 34. Known Boundary Emulation (virtual)
    Ian Vernon
    5/25/23, 9:30 AM
  27. 28. Gaussian process regression constrained by boundary value problems
    Mamikon Gulian (Sandia National Laboratories)
    5/25/23, 10:00 AM
  28. 29. Multi-output gaussian processes for inverse uncertainty quantification in neutron noise analysis (virtual)
    Paul Lartaud
    5/25/23, 11:00 AM
  29. 27. Quantification for a covariant energy density functional emulated by the reduced basis method
    Pablo Giuliani (Michigan State University)
    5/25/23, 11:30 AM
  30. 44. Hamiltonian Monte Carlo & eigenvector continuation for ab initio nuclear physics
    Andreas Ekström (Chalmers University of Technology)
    5/25/23, 12:00 PM
  31. 31. Eigenvector continuation emulators for the ab initio symmetry-adapted framework
    Kevin Becker
    5/25/23, 1:30 PM
  32. 32. Bootstrap for multivariate time series and gravitational wave detection
    Soumen Lahiri (Washington University)
    5/25/23, 2:00 PM
  33. 33. Data integration using constrained Gaussian process models with applications to nuclear physics
    Shuang Zhou (Arizona State University)
    5/25/23, 2:30 PM
  34. 35. Short Talk: Potential energy surface emulation and impact on fission trajectories
    Daniel Lay
    5/25/23, 3:30 PM
  35. 36. Short Talk: Ex fissio ad astra: extending optical models to the fission fragment region
    Kyle Beyer
    5/25/23, 3:45 PM
  36. 37. Short Talk: Reduced Basis Methods and Scattering
    Daniel Odell
    5/25/23, 4:00 PM
  37. 3. AI/ML+data science tools for detector design at the Electron Ion Collider (virtual)
    Cristiano Fanelli (William & Mary)
    5/26/23, 9:00 AM
  38. 4. Excavating insights from sparse data with statistics and machine learning
    Kyle Godbey
    5/26/23, 9:30 AM
  39. 5. Gaussian processes for autonomous data acquisition at large-scale synchrotron and neutron facilities
    Marcus Noack (Lawrence Berkeley National Laboratory)
    5/26/23, 10:00 AM
  40. 6. Deep learning techniques in ground-based imaging gamma-ray observatories (virtual)
    Daniel Nieto (Madrid University)
    5/26/23, 11:00 AM
  41. 24. Short Talk
    Yehu Chen
  42. 7. TDB
  43. 20. TDB
  44. 21. TDB
  45. 22. TDB
  46. 41. TDB
  47. 43. TDB
Building timetable...