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

22 May 2023, 08:00 → 26 May 2023, 13:30 US/Central

Washington University in St. Louis

The next international meeting on Information and Statistics in Nuclear Experiment and Theory (ISNET-9), organized by the Department of Physics at Washington University in St. Louis, will take place May 22-26, 2023 in St. Louis, Missouri. 

The mission of the ISNET community is to encourage, facilitate and develop the use of statistical and computational methodologies to enable nuclear physics to reach more quantitatively rigorous scientific conclusions. We do this by combining domain knowledge from the broad nuclear physics community with expertise in related fields of research, such as statistics, mathematics and computer science.

For 2023, ISNET-9 will be a hybrid meeting. Talks will be delivered on-site, but sessions will also be broadcast via Zoom for remote participants. Talks will take place beginning Tuesday morning the 23rd and continue through the morning of Friday the 26th

On Monday, May 22nd, we will also host the third annual BAND Camp. BAND Camp is a one-day set of pedagogical presentations organized by the Bayesian Analysis of Nuclear Dynamics collaboration (https://bandframework.github.io). Talks will be geared towards students and postdocs, and aim to provide an introduction to the software tools developed by BAND and to the corresponding concepts and methods of Bayesian uncertainty quantification. This year BAND is accepting 40 participants for an in-person camp; registrations will be accepted until that capacity is exhausted. 

On Wednesday, May 24th, we will hold a one-day machine learning symposium in honor of John Clark (Wayman Crow Professor Emeritus of Physics at WashU) to recognize his pioneering work in neural networks.

There are no conference fees for ISNET-9. A welcome reception is planned for Monday, a dinner is planned for Wednesday evening, and a poster session is planned for Thursday afternoon.

Topics:

• Statistical methods for optimization and data analysis
• Bayesian inference
• Uncertainty quantification
• Machine learning
• Emulators
• Resampling techniques
• Accelerator design

 

Local Organizing Committee:

• Maria Piarulli (chair)
• Sarah Akin
• Manel Errando
• Haijun Gong
• Soumendra Lahiri
• Andrina Nicola
• Saori Pastore
• Mary Sullivan
• Alison Verbeck
• Yajie Yuan

 

This conference is supported by the McDonnell Center for the Space Sciences, the Department of Physics, and the School of Arts & Sciences at Washington University in St. Louis. BAND Camp is supported by the BAND collaboration.

Monday 22 May

08:00 → 09:00 BAND Camp: Registration/Breakfast

09:00 → 10:45 BAND Camp: Session I

09:00 Welcome and Intro

Speaker: Maria Piarulli (Washington University)

09:05 Gaussian Process emulation using surmise

Speakers: Moses Chan (Northwestern), Özge Surer (Miami University)

ISNET2023_OS.pdf

Notebook: Emulation and calibration with Gaussian processes

10:45 → 11:15 Coffee Break

11:15 → 12:45 BAND Camp: Session II

11:15 Reduced Basis Methods for emulation and the ROSE software package

Speakers: Daniel Odell (Ohio), Pablo Giuliani (MSU/FRIB)

Notebook: ROSE Tutorial 1 - Minnesota Potential

Notebook: ROSE Tutorial 2 - Optical Potential

ROSE_BAND_CAMP.pdf

12:45 → 14:15 Lunch Break

14:15 → 15:45 BAND Camp: Session III

14:15 Bayesian Model Mixing using Taweret

Speakers: Alexandra Semposki (Ohio University), John Yannotty (Ohio State), Matt Pratola (Ohio State)

Notebook: BART Model Mixing Tutorial

Notebook: Taweret: Multivariate Model Mixing Tutorial

15:45 → 16:15 Coffee Break

16:15 → 17:30 BAND Camp: Session IV

16:15 The Bayesian Mass Explorer, BMEX

Speaker: Kyle Godbey (MSU/FRIB)

Website: BMEX - Bayesian Mass Explorer

16:55 Experimental Design and the BAND software framework

Speakers: Dick Furnstahl (Ohio State), Witek Nazarewicz (MSU/FRIB)

Furnstahl_BAND_Camp_at_ISNET_May_2023.pdf

Witek_BandCamp.pdf

18:00 → 20:00 Welcome Reception

Tuesday 23 May

08:00 → 08:45 Registration/Breakfast

08:45 → 09:00 ISNET: Welcome

Convener: Brad Jolliff (Washington University)

ISNET_Welcome_5-23-23 (1).pdf

ISNET_Welcome_5-23-23 (1).pptx

09:00 → 10:30 ISNET: Statistical methods for optimization, Bayesian inference, and uncertainty quantification

ISNET_Welcome_5-23-23 (1).pdf

ISNET_Welcome_5-23-23 (1).pptx

09:00 Recent tools and developments in Bayesian statistics

Speaker: Yuling Yao (Flatiron Institute)

SlidesYY2.pptx

SlidesYY.key

09:30 A statistical exploration of CEMP star classification with s-process models

Speaker: Andrés Yagüe López (Los Alamos National Lab)

talk_isnet_9.pdf

10:00 Global fits and Bayesian inference in "Beyond the Standard Model" physics (virtual)

Speaker: Anders Kvellestad (University of Oslo)

Kvellestad_ISNET9_2023.pdf

10:30 → 11:00 Coffee Break

11:00 → 12:30 ISNET: Statistical methods for optimization, Bayesian inference, and uncertainty quantification

ISNET_Welcome_5-23-23 (1).pdf

ISNET_Welcome_5-23-23 (1).pptx

11:00 Applications of novel chiral interactions to quantum Monte Carlo methods and astrophysical data analysis

Speaker: Rahul Somasundaram (Syracuse University)

ISNET-9.pdf

Link to google slides

11:30 Sequential Bayesian experimental design for calibration of expensive physics models

Speaker: Ozge Sürer

ISNET2023_OS.pdf

12:00 History matching for nuclear ab initio calculations

Speaker: Christian Forssén (Durham University)

forssen-ISNET-230522_with_builds.pdf

12:30 → 13:30 Lunch Break

13:30 → 15:00 ISNET: Statistical methods for optimization, Bayesian inference, and uncertainty quantification

ISNET_Welcome_5-23-23 (1).pdf

ISNET_Welcome_5-23-23 (1).pptx

13:30 Bayesian probability updates using sampling/importance resampling: applications in nuclear theory

Speaker: Weiguang Jiang (Chalmers University)

ISNET9.pdf

14:00 Hamiltonian Monte Carlo computation in spatial statistics

Speaker: Debashis Mondal (WashU)

mondal-isnet9.pdf

14:30 Bayesian model calibration for nuclear decays with the Skyrme finite-amplitude method

Speaker: Tong Li (LLNL)

ISNET9_presentation_TongLi.pdf

15:00 → 15:30 Coffee Break

15:30 → 16:30 ISNET: Statistical methods for optimization, Bayesian inference, and uncertainty quantification

ISNET_Welcome_5-23-23 (1).pdf

ISNET_Welcome_5-23-23 (1).pptx

15:30 Accounting for material and experimental variability using a random effects Bayesian inferential framework

Speaker: Denielle Ricciardi (Sandia National Laboratories)

ISNET Presentation Final.pdf

ISNET Presentation Final.pptx

16:00 Short talk: Bayesian calibration of viscous anisotropic hydrodynamic simulations of heavy-ion collisions

Speaker: Dananjaya Liyanage

Dananjaya_Liyanage_ISNET_9 2.pdf

Dananjaya_Liyanage_ISNET_9 2.pptx

16:15 Short Talk: How Uncertain Am I? Theoretical errors in Bayesian model calibration for EFTs

Speaker: Jason Bub

Bub_ISNET_2023_Slides.pdf

16:30 → 17:00 Discussion Session

Wednesday 24 May

08:25 → 08:55 Breakfast

08:55 → 10:30 ISNET: Symposium in Honor of John Clark: Different Aspects of Machine Learning

ISNET_Welcome_5-23-23 (1).pdf

ISNET_Welcome_5-23-23 (1).pptx

08:55 Prof. John W. Clark

Speaker: Henric Krawczynski

JohnClark.pptx

09:00 John Clark: Physicist at the computational frontier (virtual)

Speaker: Henrik Bohr

Presentation (John Clark)1.pdf

Presentation (John Clark)1.pptx

09:45 Machine learning for nuclear physics

Speaker: Witek Nazarewicz (MSU/FRIB)

Nazarewicz-ISNET2023.pptx

10:30 → 11:00 Coffee Break

11:00 → 12:15 ISNET: Symposium in Honor of John Clark: Different Aspects of Machine Learning

ISNET_Welcome_5-23-23 (1).pdf

ISNET_Welcome_5-23-23 (1).pptx

11:00 Machine learning of nuclear properties : a brief tribute to Prof. John Walter Clark (recording)

Speaker: Eirene Mavrommatis

E. Mavrommatis, Symposium Clark, Presentation Show (4).ppsx

11:15 Surrogate models of nuclear density functional theory with gaussian processes and autoencoders

Speaker: Marc Verriere

2023-MV-ISNET-9.pdf

11:45 Machine learning for heavy-ion accelerators (virtual)

Speaker: Yue Hao (Michigan State University)

AIML_Heavy_Ion_Accelerator.pdf

12:15 → 13:15 Lunch Break

13:15 → 14:45 ISNET: Symposium in Honor of John Clark: Different Aspects of Machine Learning

ISNET_Welcome_5-23-23 (1).pdf

ISNET_Welcome_5-23-23 (1).pptx

13:15 Nuclear masses learned from a probabilistic neural network

Speaker: Amy Lovell (Los Alamos National Lab)

ISNET9_Lovell.pdf

13:45 Machine learning for the many-body problem

Speaker: Alessandro Lovato (Argonne National laboratory)

Lovato_ISNET.pdf

14:15 Mapping out the thermodynamic stability of a QCD EOS with a critical point using active learning

Speaker: Debora Mroczek (University of Illinois)

Mroczek_ISNET_2023.pdf

14:45 → 15:15 Coffee Break

15:15 → 16:30 ISNET: Symposium in Honor of John Clark: Different Aspects of Machine Learning

ISNET_Welcome_5-23-23 (1).pdf

ISNET_Welcome_5-23-23 (1).pptx

15:15 Predicting nuclear masses with product-unit networks (virtual)

Speaker: Babette Dellen

VortragISNET.pptx

15:45 Machine learning for Deeply Virtual Compton Scattering (virtual)

Speaker: Manal Almaeen

ISNET9 (1).pdf

16:15 Short Talk: Deep learning pairing correlations from neural-network quantum states

Speaker: Jane Kim

ISNET-9 (2).pdf

16:30 → 17:00 Discussion Session

18:00 → 21:00 Social Dinner

Thursday 25 May

08:30 → 09:00 Breakfast

09:00 → 10:30 ISNET: Emulators and Resampling Techniques

ISNET_Welcome_5-23-23 (1).pdf

ISNET_Welcome_5-23-23 (1).pptx

09:00 Overview of emulators for nuclear physics

Speaker: Dick Furnstahl

Furnstahl_ISNET-9_May_2023.pdf

09:30 Known Boundary Emulation (virtual)

Speaker: Ian Vernon

ISNET 2023 Known Boundary Emulation.pdf

10:00 Gaussian process regression constrained by boundary value problems

Speaker: Mamikon Gulian (Sandia National Laboratories)

final_isnet_talk.pdf

10:30 → 11:00 Coffee Break

11:00 → 12:30 ISNET: Emulators and Resampling Techniques

ISNET_Welcome_5-23-23 (1).pdf

ISNET_Welcome_5-23-23 (1).pptx

11:00 Multi-output gaussian processes for inverse uncertainty quantification in neutron noise analysis (virtual)

Speaker: Paul Lartaud

Slides_ISNET.pdf

11:30 Quantification for a covariant energy density functional emulated by the reduced basis method

Speaker: Pablo Giuliani (Michigan State University)

RBMsISNET.pdf

RBMsISNET.pptx

12:00 Hamiltonian Monte Carlo & eigenvector continuation for ab initio nuclear physics

Speaker: Andreas Ekström (Chalmers University of Technology)

andreas_talk.pdf

12:30 → 13:30 Lunch Break

13:30 → 15:00 ISNET: Emulators and Resampling Techniques

ISNET_Welcome_5-23-23 (1).pdf

ISNET_Welcome_5-23-23 (1).pptx

13:30 Eigenvector continuation emulators for the ab initio symmetry-adapted framework

Speaker: Kevin Becker

KSB_EVC_ISNET_talk.pdf

14:00 Bootstrap for multivariate time series and gravitational wave detection

Speaker: Soumen Lahiri (Washington University)

SNL-ISNET-9.pdf

14:30 Data integration using constrained Gaussian process models with applications to nuclear physics

Speaker: Shuang Zhou (Arizona State University)

Talk_proton.pdf

15:00 → 15:30 Coffee Break

15:30 → 16:15 ISNET: Emulators and Resampling Techniques

ISNET_Welcome_5-23-23 (1).pdf

ISNET_Welcome_5-23-23 (1).pptx

15:30 Short Talk: Potential energy surface emulation and impact on fission trajectories

Speaker: Daniel Lay

ISNET 2023 2.pdf

15:45 Short Talk: Ex fissio ad astra: extending optical models to the fission fragment region

Speaker: Kyle Beyer

kbeyer_isnet9_short_talk (1).pdf

16:00 Short Talk: Reduced Basis Methods and Scattering

Speaker: Daniel Odell

Odell ISNET 2023.pdf

16:15 → 16:45 Discussion Session

16:45 → 17:45 ISNET: Poster Session

Andrew Gordeev: Bayesian parameter estimation of QGP viscosities in partial chemical equilibrium
Denielle Ricciardi: Accounting for material and experimental variability using a random effects bayesian inferential framework
Hannah Göttling: Gaussian processes for the nuclear equation of state
Mookyong Son: Variational Bayes Computer Models
Yehu Chen: Active learning for marginal effect estimation with Gaussian Process preference learning

Friday 26 May

08:30 → 09:00 Breakfast

09:00 → 10:30 ISNET: Advanced Statistics Techniques for Analyzing Experimental Data and for Accelerator design

ISNET_Welcome_5-23-23 (1).pdf

ISNET_Welcome_5-23-23 (1).pptx

09:00 AI/ML+data science tools for detector design at the Electron Ion Collider (virtual)

Speaker: Cristiano Fanelli (William & Mary)

05.26.2023_ISNET9_Fanelli.pdf

09:30 Excavating insights from sparse data with statistics and machine learning

Speaker: Kyle Godbey

ISNET Data Mining.pptx

10:00 Gaussian processes for autonomous data acquisition at large-scale synchrotron and neutron facilities

Speaker: Marcus Noack (Lawrence Berkeley National Laboratory)

10:30 → 11:00 Coffee Break

11:00 → 11:30 ISNET: Advanced Statistics Techniques for Analyzing Experimental Data and for Accelerator design

ISNET_Welcome_5-23-23 (1).pdf

ISNET_Welcome_5-23-23 (1).pptx

11:00 Deep learning techniques in ground-based imaging gamma-ray observatories (virtual)

Speaker: Daniel Nieto (Madrid University)

DNieto_isnet9-may23.pdf

11:30 → 12:00 Discussion Session: Closing/Remarks