Numerical Algorithms for Stochastic Model and Uncertainty Quantification in High-Dimensional Complex Systems - Part I of II
For Part II, see MS-Tu-E-35

Date: August 11
Time: 13:30--15:30
Room: 408

(Note: Click title to show the abstract.)

Wang, Peng (Beihang Univ.)
Lin, Guang (Purdue Univ.)

Abstract: Uncertainty persists in most natural and engineering systems, from material discovery to reactive transport in porous media. Quantifying the uncertainty associated with the parameters in complex systems is critical, which can help us to verify our modern simulation codes and assess confidence levels. Our aim is to use accurate computational simulations to predict the behaviour of complex systems. For large number of random dimensions, advanced stochastic approximation techniques are necessary to minimize the complexity of mathematical models. This minisymposium will explore recent advances in numerical algorithms and applications for stochastic model, uncertainty quantification, and model reduction in large-scale high-dimensional complex systems.

Density estimation with transport maps
Li, Jinglai (shanghai jiaotong univerisity)

Adaptive ANOVA Based Reduced Basis Methods for Partial Differential Equations with High Dimensional Random Inputs
Liao, Qifeng (ShanghaiTech Univ.)
Lin, Guang (Purdue Univ.)

The hp adaptivity of minimum action method
Wan, Xiaoliang (Louisiana State Univ.)

Enhance Sparsity through Changing the Measure
Yang, Xiu (Pacific Northwest Natl Laboratory)
Huan, Lei (Pacific Northwest Natl Laboratory)
Baker, Nathan (Pacific Northwest Natl Laboratory)
Lin, Guang (Purdue Univ.)


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