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

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

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Contribution List

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54. Welcome and Intro
Maria Piarulli (Washington University)
22/05/2023, 09:00
53. Gaussian Process emulation using surmise
Moses Chan (Northwestern), Özge Surer (Miami University)
22/05/2023, 09:05
55. Reduced Basis Methods for emulation and the ROSE software package
Daniel Odell (Ohio), Pablo Giuliani (MSU/FRIB)
22/05/2023, 11:15
56. Bayesian Model Mixing using Taweret
Alexandra Semposki (Ohio University), John Yannotty (Ohio State), Matt Pratola (Ohio State)
22/05/2023, 14:15
57. The Bayesian Mass Explorer, BMEX
Kyle Godbey (MSU/FRIB)
22/05/2023, 16:15
58. Experimental Design and the BAND software framework
Dick Furnstahl (Ohio State), Witek Nazarewicz (MSU/FRIB)
22/05/2023, 16:55
8. Recent tools and developments in Bayesian statistics
Yuling Yao (Flatiron Institute)
23/05/2023, 09:00
9. A statistical exploration of CEMP star classification with s-process models
Andrés Yagüe López (Los Alamos National Lab)
23/05/2023, 09:30
10. Global fits and Bayesian inference in "Beyond the Standard Model" physics (virtual)
Anders Kvellestad (University of Oslo)
23/05/2023, 10:00
14. Applications of novel chiral interactions to quantum Monte Carlo methods and astrophysical data analysis
Rahul Somasundaram (Syracuse University)
23/05/2023, 11:00
30. Sequential Bayesian experimental design for calibration of expensive physics models
Ozge Sürer
23/05/2023, 11:30
11. History matching for nuclear ab initio calculations
Christian Forssén (Durham University)
23/05/2023, 12:00
13. Bayesian probability updates using sampling/importance resampling: applications in nuclear theory
Weiguang Jiang (Chalmers University)
23/05/2023, 13:30
42. Hamiltonian Monte Carlo computation in spatial statistics
Debashis Mondal (WashU)
23/05/2023, 14:00
38. Bayesian model calibration for nuclear decays with the Skyrme finite-amplitude method
Tong Li (LLNL)
23/05/2023, 14:30
12. Accounting for material and experimental variability using a random effects Bayesian inferential framework
Denielle Ricciardi (Sandia National Laboratories)
23/05/2023, 15:30
39. Short talk: Bayesian calibration of viscous anisotropic hydrodynamic simulations of heavy-ion collisions
Dananjaya Liyanage
23/05/2023, 16:00
40. Short Talk: How Uncertain Am I? Theoretical errors in Bayesian model calibration for EFTs
Jason Bub
23/05/2023, 16:15
61. Prof. John W. Clark
Henric Krawczynski
24/05/2023, 08:55
15. John Clark: Physicist at the computational frontier (virtual)
Henrik Bohr
24/05/2023, 09:00
16. Machine learning for nuclear physics
Witek Nazarewicz (MSU/FRIB)
24/05/2023, 09:45
60. Machine learning of nuclear properties
: a brief tribute to Prof. John Walter Clark (recording)
Eirene Mavrommatis
24/05/2023, 11:00
47. Surrogate models of nuclear density functional theory with gaussian processes and autoencoders
Marc Verriere
24/05/2023, 11:15
45. Machine learning for heavy-ion accelerators (virtual)
Yue Hao (Michigan State University)
24/05/2023, 11:45
17. Nuclear masses learned from a probabilistic neural network
Amy Lovell (Los Alamos National Lab)
24/05/2023, 13:15
18. Machine learning for the many-body problem
Alessandro Lovato (Argonne National laboratory)
24/05/2023, 13:45
19. Mapping out the thermodynamic stability of a QCD EOS with a critical point using active learning
Debora Mroczek (University of Illinois)
24/05/2023, 14:15
46. Predicting nuclear masses with product-unit networks (virtual)
Babette Dellen
24/05/2023, 15:15
25. Machine learning for Deeply Virtual Compton Scattering (virtual)
Manal Almaeen
24/05/2023, 15:45
23. Short Talk: Deep learning pairing correlations from neural-network quantum states
Jane Kim
24/05/2023, 16:15
26. Overview of emulators for nuclear physics
Dick Furnstahl
25/05/2023, 09:00
34. Known Boundary Emulation (virtual)
Ian Vernon
25/05/2023, 09:30
28. Gaussian process regression constrained by boundary value problems
Mamikon Gulian (Sandia National Laboratories)
25/05/2023, 10:00
29. Multi-output gaussian processes for inverse uncertainty quantification in neutron noise analysis (virtual)
Paul Lartaud
25/05/2023, 11:00
27. Quantification for a covariant energy density functional emulated by the reduced basis method
Pablo Giuliani (Michigan State University)
25/05/2023, 11:30
44. Hamiltonian Monte Carlo & eigenvector continuation for ab initio nuclear physics
Andreas Ekström (Chalmers University of Technology)
25/05/2023, 12:00
31. Eigenvector continuation emulators for the ab initio symmetry-adapted framework
Kevin Becker
25/05/2023, 13:30
32. Bootstrap for multivariate time series and gravitational wave detection
Soumen Lahiri (Washington University)
25/05/2023, 14:00
33. Data integration using constrained Gaussian process models with applications to nuclear physics
Shuang Zhou (Arizona State University)
25/05/2023, 14:30
35. Short Talk: Potential energy surface emulation and impact on fission trajectories
Daniel Lay
25/05/2023, 15:30
36. Short Talk: Ex fissio ad astra: extending optical models to the fission fragment region
Kyle Beyer
25/05/2023, 15:45
37. Short Talk: Reduced Basis Methods and Scattering
Daniel Odell
25/05/2023, 16:00
3. AI/ML+data science tools for detector design at the Electron Ion Collider (virtual)
Cristiano Fanelli (William & Mary)
26/05/2023, 09:00
4. Excavating insights from sparse data with statistics and machine learning
Kyle Godbey
26/05/2023, 09:30
5. Gaussian processes for autonomous data acquisition at large-scale synchrotron and neutron facilities
Marcus Noack (Lawrence Berkeley National Laboratory)
26/05/2023, 10:00
6. Deep learning techniques in ground-based imaging gamma-ray observatories (virtual)
Daniel Nieto (Madrid University)
26/05/2023, 11:00
49. Accounting for Material and Experimental Variability Using a Random Effects Bayesian Inferential Framework
Denielle Ricciardi
48. Bayesian parameter estimation of QGP viscosities in partial chemical equilibrium
Andrew Gordeev
50. Gaussian processes for the nuclear equation of state
Hannah Göttling
59. Machine Learning of Nuclear Properties
: A Brief Tribute to Prof. John Walter Clark
Eirene Mavrommatis
51. Nuclear Binding energy prediction using Deep Learning on AME2020 dataset
Ramesh Bhandari
24. Short Talk
Yehu Chen
2. Statistical tools and ML for accelerator design
7. TDB
20. TDB
21. TDB
22. TDB
41. TDB
43. TDB
52. Variational Bayes Computer Models
Mookyong Son
1. Welcome
Brad Joliff (WashU)
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