Joseph L. Hart

Works (8)

Updated: July 5th, 2023 15:37

2021 journal article

Randomized algorithms for generalized singular value decomposition with application to sensitivity analysis

NUMERICAL LINEAR ALGEBRA WITH APPLICATIONS, 28(4).

By: A. Saibaba n , J. Hart* & B. Bloemen Waanders*

author keywords: generalized singular value decomposition; iterative methods; randomized algorithms; sensitivity analysis
TL;DR: New randomized algorithms for computing the GSVD which use randomized subspace iteration and weighted QR factorization are proposed, motivated by applications in hyper‐differential sensitivity analysis (HDSA). (via Semantic Scholar)
Sources: Web Of Science, NC State University Libraries
Added: March 22, 2021

2019 journal article

Global Sensitivity Analysis for Statistical Model Parameters

SIAM-ASA JOURNAL ON UNCERTAINTY QUANTIFICATION, 7(1), 67–92.

By: J. Hart n, J. Bessac * & E. Constantinescu *

author keywords: global sensitivity analysis; dimension reduction; Markov chain Monte Carlo; correlated parameters
TL;DR: A novel framework is introduced that enables GSA for statistical model parameters and finds non-influential parameters are discovered and a reduced model with equal or stronger predictive capability is constructed by using only 79 parameters. (via Semantic Scholar)
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Source: Web Of Science
Added: April 9, 2019

2019 journal article

Global Sensitivity Analysis of High-Dimensional Neuroscience Models: An Example of Neurovascular Coupling

BULLETIN OF MATHEMATICAL BIOLOGY, 81(6), 1805–1828.

By: J. Hart n, P. Gremaud n & T. David *

author keywords: Neurovascular coupling; Global sensitivity analysis; Model parameters
MeSH headings : Actomyosin / physiology; Algorithms; Animals; Blood Flow Velocity / physiology; Computer Simulation; Extracellular Space / physiology; Humans; Mathematical Concepts; Models, Neurological; Neurovascular Coupling / physiology; Potassium / physiology; Signal Transduction / physiology; Vascular Resistance / physiology
TL;DR: Global sensitivity analysis provides a measure of the influence of each parameter, for each of the three QoIs, giving insight into areas of possible physiological dysfunction and areas of further investigation. (via Semantic Scholar)
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Source: Web Of Science
Added: June 17, 2019

2019 journal article

ROBUSTNESS OF THE SOBOL' INDICES TO DISTRIBUTIONAL UNCERTAINTY

INTERNATIONAL JOURNAL FOR UNCERTAINTY QUANTIFICATION, 9(5), 453–469.

By: J. Hart* & P. Gremaud n

author keywords: global sensitivity analysis; Sobol' indices; uncertain distributions; deep uncertainty
TL;DR: The robustness of the Sobol' indices, a commonly used tool in GSA, to changes in the distribution of the uncertain variables is analyzed, which requires minimal user specification and no additional evaluations of the model. (via Semantic Scholar)
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Source: Web Of Science
Added: January 6, 2020

2019 journal article

Robustness of the Sobol' Indices to Marginal Distribution Uncertainty

SIAM-ASA JOURNAL ON UNCERTAINTY QUANTIFICATION, 7(4), 1224–1244.

By: J. Hart* & P. Gremaud*

author keywords: global sensitivity analysis; Sobol' indices; robustness; epistemic uncertainty
TL;DR: A novel method which uses "optimal perturbations" of the marginal probability density functions to analyze the robustness of the Sobol' indices is presented. (via Semantic Scholar)
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Source: Web Of Science
Added: August 10, 2020

2018 journal article

AN APPROXIMATION THEORETIC PERSPECTIVE OF SOBOL' INDICES WITH DEPENDENT VARIABLES

INTERNATIONAL JOURNAL FOR UNCERTAINTY QUANTIFICATION, 8(6), 483–493.

By: J. Hart* & P. Gremaud n

author keywords: global sensitivity analysis; Sobol' indices; dependent variables
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Source: Web Of Science
Added: December 10, 2018

2017 journal article

EFFICIENT COMPUTATION OF SOBOL' INDICES FOR STOCHASTIC MODELS

SIAM JOURNAL ON SCIENTIFIC COMPUTING, 39(4), A1514–A1530.

By: J. Hart*, A. Alexanderian* & P. Gremaud*

author keywords: global sensitivity; Sobol' indices; stochastic models; surrogate models; MARS; high dimensions
TL;DR: This work presents a new global sensitivity analysis approach for stochastic models, i.e., models with both uncertain parameters and intrinsic Stochasticity, which relies on an analysis of variance through a generalization of Sobol' indices and on the use of surrogate models. (via Semantic Scholar)
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Source: Web Of Science
Added: August 6, 2018

2016 journal article

Transcranial Doppler-Based Surrogates for Cerebral Blood Flow: A Statistical Study

PLOS ONE, 11(11).

By: J. Hart n, V. Novak *, C. Saunders* & P. Gremaud n

MeSH headings : Aged; Algorithms; Blood Flow Velocity / physiology; Cerebral Arteries / diagnostic imaging; Cerebrovascular Circulation / physiology; Female; Humans; Magnetic Resonance Angiography; Male; Middle Aged; Models, Statistical; Ultrasonography, Doppler, Transcranial
TL;DR: A cerebral blood flow estimator is constructed based on transcranial Doppler blood flow velocity and ten other easily available patient characteristics and clinical parameters to test the hypothesis that perfusion in a given cerebral territory can be inferred from Blood Flow Velocity measurements in the corresponding stem artery. (via Semantic Scholar)
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Source: Web Of Science
Added: August 6, 2018

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