Argonne Mini-Workshop on Monte Carlo Methods

US/Central
362

362

Description

Building 362, Room F-108

Zoom coordinates:

https://argonne.zoomgov.com/j/1605870003?pwd=eG1TN1QwQkp2MGhyUEs3cDVLMmZZdz09

Meeting ID: 160 587 0003

Passcode: 559683

    • 8:30 AM 10:00 AM
      Morning Session
      Convener: Pavel Nadolsky (Southern Methodist University)
      • 8:30 AM
        Parton Distributions and Inverse Problems in HEP 30m
        Speaker: Timothy Hobbs (Argonne National Laboratory)
      • 9:00 AM
        Representative MC Sampling for PDFs 30m
        Speaker: Aurore Courtoy (Instituto de Física, UNAM)
      • 9:30 AM
        Monte Carlo Methods and Lattice QCD 30m
        Speaker: Xiaoyong Jin (ANL)
    • 10:00 AM 10:30 AM
      Coffee 30m

      Discussion and seminar setup

    • 10:30 AM 11:30 AM
      Faster Monte Carlo via Low Discrepancy Sampling 1h

      Estimating an expectation or integral is important in high energy physics, Bayesian inference, image rendering, quantitative finance, and uncertainty quantification. Monte Carlo type methods are commonly used. The numerical error can be expressed as a product of three quantities: one measuring the deficit in the sampling scheme, a second measuring the roughness of the function defining the expectation or integral, and a third representing the confounding between that function and the sampling deficit. We explain how low discrepancy sampling, also known as the quasi-Monte Carlo method, can substantially improve the efficiency of these calculations. We discuss how to improve efficiency via transformations of the integral. Our data-driven error bounds advise the user when to stop simulating. We illustrate low discrepancy sampling via our QMCPy software library (qmcpy.org).

      Speaker: Prof. Fred Hickernell (Illinois Institute of Technology)
    • 12:00 PM 1:30 PM
      Lunch 1h 30m
    • 1:30 PM 3:00 PM
      Afternoon Session
      Convener: Aurore Courtoy (Instituto de Física, UNAM)
      • 1:30 PM
        Monte Carlo Methods and Gaussian Processes 30m
        Speaker: Dr Vishwas Rao (Argonne National Laboratory)
      • 2:00 PM
        Monte Carlo for Theory and Event Generation in HEP 30m
        Speaker: Joshua Isaacson (FNAL)
      • 2:30 PM
        Variational Monte Carlo and Neural-Network Quantum States for Nuclear Simulation 30m
        Speaker: Alessandro Lovato (ANL)
    • 3:00 PM 4:00 PM
      Discussion
      • 3:00 PM
        Monte Carlo for HEP Experiments 15m
        Speaker: Sergei Chekanov (ANL)
      • 3:15 PM
        Open discussion 45m
        Speaker: Dr Timothy Hobbs (Argonne National Laboratory)