SIAM Workshop on Parameter Space Dimension Reduction(DR17)
The SIAM Workshop on Parameter Space Dimension Reduction will take place at the Omni William Penn Hotel, which is located four blocks from the David L. Lawrence Convention Center (DLCC). SIAM Annual Meeting (AN17), SIAM Conference on Control and Its Applications (CT17), SIAM Conference on Industrial and Applied Geometry (GD17) , and SIAM Workshop on Network Science (NS17) will take place at the DLCC.
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Organizing Committee
Workshop Co-Chairs
Paul G. Constantine, Colorado School of Mines, USA
David F. Gleich, Purdue University, USA
Organizing Committee
Juan J. Alonso, Stanford University, USA
Nathan Baker, Pacific Northwest National Laboratory, USA
R. Dennis Cook, University of Minnesota, USA
Emilie Dufresne, Oxford University, United Kingdom
Michael S. Eldred, Sandia National Laboratories, USA
Michael Frenklach, University of California Berkeley, USA
Roger Ghanem, University of Southern California, USA
Omar Ghattas, University of Texas at Austin, USA
Mark Girolami, Imperial College London, United Kingdom
Gianluca Iaccarino, Stanford University, USA
Youssef Marzouk, Massachusetts Institute of Technology, USA
Gianluigi Rozza, Scuola Internazionale Superiore di Studi Avanti, Italy
Ralph Smith, North Carolina State University, USA
Michael Wakin, Colorado School of Mines, USA
Rachel Ward, University of Texas at Austin, USA
Brian J. Williams, Los Alamos National Laboratory, USA
Description
Complex computational science and engineering models contain several parameters representing physical inputs. Parameter studies, such as uncertainty quantification or design optimization, need to evaluate many models at different parameter values to endow predictions with credibility and confidence. However, the cost of parameter studies may grow exponentially with the number of inputs. One way to enable parameter studies in highly parameterized models is to identify low-dimensional structures in the map from input parameters to output predictions.
The DR17 workshop brings together researchers across mathematics, statistics, and engineering to explore a range of emerging techniques for parameter space dimension reduction. Topics of interest include:
- active subspaces
- basis adaptation
- inverse regression
- sufficient dimension reduction
- sloppy models
- sensitivity analysis
- ridge recovery and approximation
- deterministic and statistical parameter estimation
- applications with science and engineering simulations or data sets
