Non-linear model reduction for uncertainty quantification in large-scale inverse problems

International Journal for Numerical Methods in Engineering

Volumn 81, Issue 12, 2010, Pages 1581-1608

  Galbally, D ^a   Fidkowski, K ^b   Willcox, K ^c   Ghattas, O ^d  

^a IBERDROLA   (Spain)

^b UNIVERSITY OF MICHIGAN   (United States)

^c MASSACHUSETTS INSTITUTE OF TECHNOLOGY   (United States)

^d UNIVERSITY OF TEXAS AT AUSTIN   (United States)

Author keywords

Advection diffusion reaction PDE; Bayesian inference; Inverse problems; Non linear model reduction

Indexed keywords

ADVECTION-DIFFUSION-REACTION; BAYESIAN INFERENCE; COEFFICIENT-FUNCTION APPROXIMATION; COMBUSTION PROBLEMS; COMPUTATIONAL COSTS; CPU TIME; DISCRETE EQUATIONS; FINITE ELEMENT MODELS; FINITE-ELEMENT DISCRETIZATION; HIGH FIDELITY; INPUT PARAMETER; INTERPOLATION POINTS; MARKOV CHAIN MONTE CARLO; MODEL REDUCTION; NON-LINEAR; NON-LINEAR MODEL; ORDERS OF MAGNITUDE; POINT ESTIMATION; PROPER ORTHOGONAL DECOMPOSITIONS; REDUCED MODEL; SPATIAL DIMENSION; STATISTICAL INVERSE PROBLEMS; THREE-DIMENSIONAL PROBLEMS; UNCERTAINTY QUANTIFICATIONS;

ADVECTION; AIRCRAFT ENGINES; BAYESIAN NETWORKS; COMPUTATIONAL FLUID DYNAMICS; COST REDUCTION; DIFFUSION; FINITE ELEMENT METHOD; INFERENCE ENGINES; INTERPOLATION; MARKOV PROCESSES; MATHEMATICAL MODELS; ORTHOGONAL FUNCTIONS; PARTIAL DIFFERENTIAL EQUATIONS; THREE DIMENSIONAL;

INVERSE PROBLEMS;

EID: 75649118920     PISSN: 00295981     EISSN: 10970207     Source Type: Journal    
DOI: 10.1002/nme.2746     Document Type: Article

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