Integration of Data and Simulation

This theme encompasses the many computational tasks and tools required to integrate experimental and numerical inquiries, from parameter estimation and inverse problems to real-time data assimilation. This theme, like many of the themes, includes and combines both statistical/stochastic and deterministic approaches.


Related Publications

Merging multiple partial-depth data time series using objective empirical orthogonal function fitting
Y-T Lin, AE Newhall, TF Duda, PFJ Lermusiaux, and PJ Haley. IEEE Journal of Oceanic Engineering, 35(4):710-721, 2010.

Non-linear model reduction for uncertainty quantification in large-scale inverse problems
D Galbally, K Fidkowski, K Willcox, & O Ghattas. International Journal for Numerical Methods in Engineering, 81(12):1581-1608, 2010.

Dynamically orthogonal field equations for continuous stochastic dynamical systems
TP Sapsis and PFJ Lermusiaux. Physica D, 238(23-24):2347-2360, 2009.

Dimensionality reduction and polynomial chaos acceleration of Bayesian inference in inverse problems
YM Marzouk and HN Najm. Journal of Computational Physics, 228(6): 1862–1902, 2009.

Inverse barotropic tidal estimation for regional ocean applications
OG Logutov and PFJ Lermusiaux.Ocean Modelling, 25(1-2):17-34, 2008.

Hessian-based model reduction for large-scale systems with initial-condition inputs
O Bashir, K Willcox, O Ghattas, B van Bloemen Waanders, and J Hill. International Journal for Numerical Methods in Engineering, 73(6):844-868, 2008.

Unsteady flow sensing and estimation via the gappy proper orthogonal decomposition
K Willcox. Computers & Fluids, 35(2):208-226, 2006.

Aerodynamic data reconstruction and inverse design using proper orthogonal decomposition
T Bui-Thanh, M Damodaran, & K Willcox. AIAA Journal, 42(8):1505-1516, 2004.