Global sensitivity analysis informed model reduction and selection applied to a Valsalva maneuver model

Randall, E. Benjamin; Randolph, Nicholas; Alexanderian, Alen; Olufsen, Mette S.

doi:10.1016/j.jtbi.2021.110759

Cited by 7 publications

(4 citation statements)

References 31 publications

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“…The Sobol’ indices were calculated using Monte Carlo integration by computing 10 3 (16+2)=1.8e4 model evaluations similar to the procedure described in Randall et al . (2021). We then ranked the total effect Sobol’ indices (Fig.…”

Section: Methodsmentioning

confidence: 99%

“…Sobol' indices apportion the variance in the output to the effect of each parameter (Sobol , 2001). In particular, we use total effect Sobol' indices to characterize the effect of both the parameter and parameter interactions on the residual variance (Randall et al 2021). All parameters were varied within their physiological bounds, listed in Table S1 in the Supplemental Material file in Supporting Information.…”

Section: Mathematical Modelling Frameworkmentioning

confidence: 99%

“…The residual (eqn (S80) in the Supplemental Material file in Supporting Information) was calculated by determining the least squares error between simulations and RHC and TTE data in a similar fashion to that described in Colunga et al (2020). The Sobol' indices were calculated using Monte Carlo integration by computing 10 3 (16+2)=1.8e4 model evaluations similar to the procedure described in Randall et al (2021). We then ranked the total effect Sobol' indices (Fig.…”

Section: Mathematical Modelling Frameworkmentioning

confidence: 99%

See 2 more Smart Citations

Phenotyping heart failure using model‐based analysis and physiology‐informed machine learning

Jones

Randall

Hummel

et al. 2021

The Journal of Physiology

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Section: Methodsmentioning

confidence: 99%

Section: Mathematical Modelling Frameworkmentioning

confidence: 99%

Section: Mathematical Modelling Frameworkmentioning

confidence: 99%

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Phenotyping heart failure using model‐based analysis and physiology‐informed machine learning

Jones

Randall

Hummel

et al. 2021

The Journal of Physiology

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“…One or more parameters could be an order of magnitude more or less influential than the rest. Mathematically, the study of parameter influence is known as sensitivity analysis and is an active area of research [57][58][59][60][61][62][63]. Broadly, sensitivity analysis can be divided into two categories:…”

Section: Sensitivity Analysismentioning

confidence: 99%

Adaptive basis sets for practical quantum computing

Kwon

Curtin

Morrow

et al. 2023

Int J of Quantum Chemistry

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Electronic structure calculations on small systems such as H 2 , H 2 O, LiH, and BeH 2 with chemical accuracy are still a challenge for the current generation of noisy intermediate-scale quantum (NISQ) devices. One of the reasons is that due to the device limitations, only minimal basis sets are commonly applied in quantum chemical calculations, which allows one to keep the number of qubits employed in the calculations at a minimum. However, the use of minimal basis sets leads to very large errors in the computed molecular energies as well as potential energy surface shapes. One way to increase the accuracy of electronic structure calculations is through the development of small basis sets better suited for quantum computing. In this work, we show that the use of adaptive basis sets, in which exponents and contraction coefficients depend on molecular structure, provides an easy way to dramatically improve the accuracy of quantum chemical calculations without the need to increase the basis set size and thus the number of qubits utilized in quantum circuits. As a proof of principle, we optimize an adaptive minimal basis set for quantum computing calculations on an H 2 molecule, in which exponents and contraction coefficients depend on the H H distance, and apply it to the generation of H 2 potential energy surface on IBM-Q quantum devices. The adaptive minimal basis set reaches the accuracy of the double-zeta basis sets, thus allowing one to perform double-zeta quality calculations on quantum devices without the need to utilize twice as many qubits in simulations. This approach can be extended to other molecular systems and larger basis sets in a straightforward manner.

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Mixed uncertainty analysis on pumping by peristaltic hearts using Dempster–Shafer theory

He,

Battista,

Waldrop

2024

J. Math. Biol.

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Global sensitivity analysis informed model reduction and selection applied to a Valsalva maneuver model

Cited by 7 publications

References 31 publications

Phenotyping heart failure using model‐based analysis and physiology‐informed machine learning

Phenotyping heart failure using model‐based analysis and physiology‐informed machine learning

Adaptive basis sets for practical quantum computing

Mixed uncertainty analysis on pumping by peristaltic hearts using Dempster–Shafer theory

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