A non-linear dimension reduction methodology for generating data-driven stochastic input models

Journal of Computational Physics

Volumn 227, Issue 13, 2008, Pages 6612-6637

  Ganapathysubramanian, Baskar ^a   Zabaras, Nicholas ^a  

^a Cornell University   (United States)

Author keywords

Collocation methods; Data driven models; Manifold learning; Microstructure; Model reduction; Non linear embedding; Sparse grids; Stochastic partial differential equations

Indexed keywords

DATA REDUCTION; DIFFUSION; FINITE ELEMENT METHOD; FUNCTIONALLY GRADED MATERIALS; GEOMETRY; GRAPH THEORY; HEAT CONDUCTION; MAPPING; METADATA; PARTIAL DIFFERENTIAL EQUATIONS; POLYCRYSTALLINE MATERIALS; POROUS MATERIALS; STOCHASTIC MODELS; STOCHASTIC SYSTEMS; STRUCTURAL ANALYSIS; THERMAL DIFFUSIVITY;

COLLOCATION METHOD; DATA-DRIVEN MODEL; LINEAR EMBEDDING; LOW DIMENSIONAL; MANIFOLD LEARNING; MODEL REDUCTION; NON LINEAR; NON-LINEAR EMBEDDING; SPARSE GRID; STOCHASTIC PARTIAL DIFFERENTIAL EQUATION;

MICROSTRUCTURE;

EID: 44149100436     PISSN: 00219991     EISSN: 10902716     Source Type: Journal    
DOI: 10.1016/j.jcp.2008.03.023     Document Type: Article

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