Recent Advances in Stochastic Approximation for Uncertainty Quantification: Analysis and Computation - Part I of II
For Part II, see MS-Th-BC-18

Date: August 12
Time: 16:00--18:00
Room: 209B

(Note: Click title to show the abstract.)

Archibald, Rick (Oak Ridge National Laboratory)
Webster, Clayton (Oak Ridge National Laboratory)
Zhang, Guannan (Oak Ridge National Laboratory)

Abstract: This mini-symposium focuses on the fundamental problem of how to accurately approximate solutions of both forward and inverse complex systems with random inputs. Predicting the behavior of complex phenomena relies on constructing solutions in terms of high dimensional spaces, particularly in the case when the input data are affected by large amounts of uncertainties. The resulting explosion in computational effort is a symptom of the curse of dimensionality. This mini-symposium aims at exploring recent advances in high-dimensional approximation, sparse polynomial approximation, multilevel methods, model calibration and data-driven reduced order modeling.

Reconstruction of Partially Sampled Fourier Data Using Sparse Polynomial Annihilation Sparsifying Transform
Archibald, Rick (Oak Ridge National Laboratory)

Efficient Calibration for Imperfect Computer Models
Tuo, Rui (Chinese Acad. of Sci.)
Wu, Jeff (Georgia Inst. of Tech.)

Gaussian Process Emulators in Bayesian Inverse Problems
Teckentrup, Aretha (Univ. of Warwick)
Stuart, Andrew (Univ. of Warwick)

Analysis of quasi-optimal polynomial approximations for high-dimensional parameterized PDEs
Webster, Clayton (Oak Ridge National Laboratory)
Zhang, Guannan (Oak Ridge National Laboratory)


Code: Type-Date-Time-Room No.
Type : IL=Invited Lecture, SL=Special Lectures, MS=Minisymposia, IM=Industrial Minisymposia, CP=Contributed Papers, PP=Posters
Date: Mo=Monday, Tu=Tuesday, We=Wednesday, Th=Thursday, Fr=Friday
Time : A=8:30-9:30, B=10:00-11:00, C=11:10-12:10, BC=10:00-12:10, D=13:30-15:30, E=16:00-18:00, F=19:00-20:00, G=12:10-13:30, H=15:30-16:00
Room No.: TBA