High Dimensional Model Representations

By Genyuan Li, C. Rosenthal, Herschel A. Rabitz. 2001

506 citations recorded by the source corpus.

Subfields: Molecular Biology, Nuclear and High Energy Physics, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/0f64f02574a32d5883376db1dfdfad2ea5eb4a09

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In Molecular Biology

In Nuclear and High Energy Physics

In Statistical and Nonlinear Physics

Citations in the corpus

Survey of modeling and optimization strategies to solve high-dimensional design problems with computationally-expensive black-box functions (2010, methodology): “Wang et al. (2003) and Li et al. (2006) presented random sampling HDMR. Mp-cut-HDMRs (Li et al. 2001b; monomial based preconditioned HDMR) were developed to improve features of Cut-HDMR.”
An exploration of alternative approaches to the representation of uncertainty in model predictions (2003, background): “As another possibility, Sobol’ variance decomposition [131–133] might provide a useful tool for sensitivity analysis in the context of evidence theory, with the incompletely specified probability spaces bounded by belief and plausibility giving rise to a range of variance decompositions for each unc”
In the Faculty of Science (2014, methodology): “This can be observed in the indicator program sessions in the 2005 Commonwealth conference, in papers like Meadows (2005), Scott (2005) and Kahimbaara (2005), where SIs are being integrated into planning policy and the activities of statistical organizations.”
A new class of wavelet networks for nonlinear system identification (2005, methodology): “Although the ANOVA decomposition of the NARX model (1) involves up to different functional components, experience shows that a truncated representation containing the components up to the bivariate or tri-variate functional terms often provides a satisfactory description of for many high dimensional”
Hybrid high dimensional model representation (HHDMR) on the partitioned data (2006, methodology): “Hence, a divide-and-conquer algorithm is needed and high dimensional model representation (HDMR) method is developed [7,6,1,4,3] .”
Survey of sampling-based methods for uncertainty and sensitivity analysis (2006, methodology): “(6-61) and also calculated with the previously indicated Monte Carlo procedure.”
Generalized Sobol sensitivity indices for dependent variables: numerical methods (2013, unknown): “with the concept of HDMR.[12,13] In [12], the authors suggest to reconstruct the model function using classical bases (polynomials, splines,.”