Location
This workshop was held at the Penn Pavilion (Level 2), Duke University, Durham, NC.
Description
This Opening Workshop marked the official start of the 2017-18 SAMSI Program on Quasi-Monte Carlo and High-Dimensional Sampling Methods for Applied Mathematics. The first workshop day consisted of tutorials that introduced QMC methods, followed by a poster reception. The remaining days featured research presentations, discussion panels, and the formation of (virtual) research working groups.
Speakers
Tutorial Presentations
- Fred Hickernell (Illinois Institute of Technology)
- Frances Kuo (University of New South Wales Sydney, Australia)
- Pierre L’Ecuyer (Université de Montréal, Canada)
- Art Owen (Stanford University)
Research Talks
- Mahadevan Ganesh (Colorado School of Mines)
- Mathieu Gerber (University of Bristol, UK)
- Mike Giles (University of Oxford, UK)
- Mac Hyman (Tulane University)
- Peter Kritzer (Johann Radon Institute, Austria)
- Lester Mackey (Microsoft Research New England)
- Simon Mak (Georgia Institute of Technology)
- Dirk Nuyens (KU Leuven, Belgium)
- Chris Oates (University of Newcastle Upon Tyne and Alan Turing Institute, UK)
- Clementine Prieur (Université Grenoble Alpes, France)
- Florian Puchhammer (Johannes Kepler Universität Linz, Austria)
- Christoph Schwab (ETH Zürich, Switzerland)
- Ian Sloan (University of New South Wales Sydney, Australia)
- Roshan Vengazhiyil (Georgia Institute of Technology)
- Grzegorz Wasilkowski (University of Kentucky)
- Clayton Webster (Oakridge National Laboratory)
- Henryk Woźniakowski (Columbia University, and University of Warsaw, Poland)
- Dongbin Xiu (Ohio State University)
- Guannan Zhang (Oakridge National Laboratory)
Schedule and Supporting Media
Printable Schedule
Speaker Abstracts
Participant List
Poster Titles
Monday, August 28, 2017
Penn Pavilion, West Campus, Duke University
| Description | Speaker | Slides | Videos |
|---|---|---|---|
| Introductions and Welcome | Ilse Ipsen, Associate Director, SAMSI | video | |
| Tutorial: Introduction to Quasi-Monte Carlo | Art Owen, Stanford University | video | |
| Tutorial: Lattice Rules for Quasi-Monte Carlo | Pierre L’Ecuyer, University of Montreal (CAN) | video | |
| Tutorial: Error Analysis for Quasi-Monte Carlo Methods | Fred Hickernell, Illinois Institute of Technology | video | |
| Working Groups at SAMSI | Ilse Ipsen, Associate Director, SAMSI | ||
| Tutorial: Application of QMC to PDEs with Random Coefficients — a survey of analysis and implementation | Frances Kuo, UNSW-Sydney (AUS) | video | |
| Tutorial: Introduction to Global Sensitivity | Clémentine Prieur, Grenoble Alpes University (FRA) | video | |
| Poster Session and Reception |
Tuesday, August 29, 2017
Penn Pavilion, West Campus, Duke University
| Description | Speaker | Slides | Videos | |
|---|---|---|---|---|
| Bayesian Probabilistic Numerical Methods (Part I) | Chris Oates, Newcastle University (ENG) | |||
| Bayesian Probabilistic Numerical Methods (Part II) | Tim Sullivan, Free University of Berlin / Zuse Institute Berlin (GER) | |||
| New Problems and Algorithms at the Interface of Optimal Transport, Statistics and Operations Research | Jose Blanchet, Stanford University | |||
| Measuring Sample Quality with Stein’s Method | Lester Mackey, Microsoft Research | |||
| High Accuracy Algorithms for Interpolating and Integrating Multivariate Functions Defined by Sparse Samples in High Dimensions | James (Mac) Hyman, Tulane University | |||
| Support Points – a new way to compact distributions | Simon Mak, Georgia Institute of Technology |
Wednesday, August 30, 2017
Penn Pavilion, West Campus, Duke University
| Description | Speaker | Slides | Videos |
|---|---|---|---|
| Sequential Function Approximation in High Dimensions with Big Data | Dongbin Xiu, Ohio State University | ||
| Sparse Polynomial Approximation via Compressed Sensing of High Dimension Functions | Clayton Webster, Oak Ridge National Laboratory | ||
| Probabilistic Numerical Methods for High-Dimensional Partial Integral Differential Equations | Guannan Zhang, Oak Ridge National Laboratory | ||
| ∞-Variate Integration | Grzegorz Wasilkovski, University of Kentucky | ||
| Quasi-polynomial Tractability of Linear Tensor Products using Function Values | Henryk Wozniakovski, Columbia University | ||
| Introduction to Sequential quasi-Monte Carlo | Mathieu Gerber, University of Bristol |
Thursday, August 31, 2017
Penn Pavilion, West Campus, Duke University
| Description | Speaker | Slides | Videos |
|---|---|---|---|
| Deterministic Sampling for Bayesian Computation | Roshan Vengazhiyil, Georgia Institute of Technology | ||
| Numerical Integration in Hermite Spaces | Peter Kritzer, Austrian Academy of Sciences (AUSTRIA) | ||
| Lower Bounds for the Discrepancy of Point Sets and Sequences | Florian Puchhammer, University of Montreal (CAN) | ||
| Higher-Order Convergence for Integration on R | Dirk Nuyens, KU Leuven (BEL) | ||
| A Sign-definite Heterogeneous Media Wave Propagation Model | Mahadevan Ganesh, Colorado School of Mines |
Friday, September 1, 2017
Penn Pavilion, West Campus, Duke University
| Description | Speaker | Slides | Videos |
|---|---|---|---|
| Multilevel QMC for Forward and Inverse UQ | Christoph Schwab, SAM, ETH Zurich (CH) | ||
| QMC and Thinning for Empirical Datasets | Mike Giles, Oxford University | ||
| Generating Random Fields the Circulant Way | Ian Sloan, UNSW-Sydney (AUS) | ||
| Wrap Up and How to Proceed from Here | Ilse Ipsen, Associate Director, SAMSI |
Questions: email [email protected]