Uncertainty quantification for acoustic wave propagation in a shallow water environment

Khazaie, Shahram; Wang, Xun; Komatitsch, Dimitri; Sagaut, Pierre

doi:10.1016/j.wavemoti.2019.102390

Cited by 7 publications

(6 citation statements)

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“…Assuming that u = (u 1 , …, u M ) are independent and follow a distribution with support set

[u_{m}^{\min}, u_{m}^{\max}]

, the MC simulation method can be employed to take N samples of u . However, this method is inefficient in terms of convergence rate (Khazaie et al., 2019). On the other hand, QMC is able to solve the problem using quasi‐random low‐discrepancy sequences to more efficiently represent the space of u (Morokoff & Caflisch, 1995).…”

Section: Methodsmentioning

confidence: 99%

“…They are problem-dependent due to the shape (nonlinearity) of system output and the number WANG 10.1029/2020WR028975 14 of 26 of uncertain parameters. It has been shown in (Khazaie et al, 2019;Schobi et al, 2015) that PC-Kriging is more efficient than the PCE and ordinary Kriging methods for some types of output functions, but for some other problems, PC-Kriging is not the optimal choice due to the effect of over-fitting (Schobi et al, 2015).…”

Section: Surrogate Modeling and Its Reliability Assessmentmentioning

confidence: 99%

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Uncertainty Quantification and Global Sensitivity Analysis for Transient Wave Propagation in Pressurized Pipes

Wang

2021

Water Resources Research

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Several approaches have been attempted for more systematical uncertainty quantification (UQ) of pipeline transient wave propagation. Considering the uncertainties on pre-transient (steady-state) pressure or flow velocity, the polynomial chaos expansion (PCE; Sattar & El-Beltagy, 2017) and the method of distributions (Alawadhi et al., 2018) are respectively employed to model the uncertainties of transient pressure. However, the methodologies in (Alawadhi et al., 2018; Sattar & El-Beltagy, 2017) use, explicitly or implicitly, the property that the transient wave varies linearly with the steady-state pressure and flow velocity, thus cannot

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“…Assuming that u = (u 1 , …, u M ) are independent and follow a distribution with support set

[u_{m}^{\min}, u_{m}^{\max}]

Section: Methodsmentioning

confidence: 99%

Section: Surrogate Modeling and Its Reliability Assessmentmentioning

confidence: 99%

Uncertainty Quantification and Global Sensitivity Analysis for Transient Wave Propagation in Pressurized Pipes

Wang

2021

Water Resources Research

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“…Then, using the experimental design

boldX

and the values of the wrapping degree

{\overset{true˜}{f}}_{i}

, a maximum likelihood optimization problem is solved to calculate

bold-italicℓ

58 . The latter is then used to obtain the coefficients of the trend

\overset{a}{true}

, the mean and the variance of the Kriging predictor (see Reference 59 for more details).…”

Section: Surrogate Modeling and Sensitivity Analysismentioning

confidence: 99%

Influence of the mechanical and geometrical parameters on the cellular uptake of nanoparticles: A stochastic approach

Iaquinta

Khazaie

Ishow

et al. 2022

Numer Methods Biomed Eng

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Nanoparticles (NPs) are used for drug delivery with enhanced selectivity and reduced side‐effect toxicity in cancer treatments. Based on the literature, the influence of the NPs mechanical and geometrical properties on their cellular uptake has been studied through experimental investigations. However, due to the difficulty to vary the parameters independently in such a complex system, it remains hard to efficiently conclude on the influence of each one of them on the cellular internalization of a NP. In this context, different mechanical / mathematical models for the cellular uptake of NPs have been developed. In this paper, we numerically investigate the influence of the NP's aspect ratio, the membrane tension and the cell‐NP adhesion on the uptake of the NP using the model introduced in1 coupled with a numerical stochastic scheme to measure the weight of each one of the aforementioned parameters. The results reveal that the aspect ratio of the particle is the most influential parameter on the wrapping of the particle by the cell membrane. Then the adhesion contributes twice as much as the membrane tension. Our numerical results match the previous experimental observations.

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“…The main drawback of those methods is that their computational complexities relate to the number of uncertain inputs, resulting in the curse of dimensionality. As mentioned in [4], the computational complexity of the modified intrusive generalized polynomial chaos expansion (gPCE) [8]- [11], [14] method increases significantly with the number of input uncertain parameters. As a result, the improved intrusive gPCE method is not readily available in practice when the number of input parameters is greater than three.…”

Section: Introductionmentioning

confidence: 99%

A Multi-Scale Attention Network for Uncertainty Analysis of Ground Penetrating Radar Modeling

2023

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A multi-scale attention-based model (MSAM) is proposed as a surrogate model for uncertainty analysis (UA) in ground penetrating radar (GPR) simulation. Instead a thousand of full-wave simulations, the surrogate model converts the uncertain inputs to electric fields, and the output uncertainty is effectively quantified. Global feature aggregation (GFA) module and local affinity reconstruction (LAR) are presented to improve the model representation capability by Affinity calculation under different receptive fields. In addition, a new loss function is proposed to accelerate the convergence of the model for training data with a wider range of input disturbances. The UA result from the Monte Carlo method (MCM) validates the effectiveness of the surrogate model. In comparison with existing deep learning methods, the proposed method can efficiently get higher quality predictions. Meanwhile, the Sobol indices evaluated by MSAM accord with those of MCM, which needs running the full-wave simulation one thousand times to converge.

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Uncertainty quantification for acoustic wave propagation in a shallow water environment

Cited by 7 publications

References 50 publications

Uncertainty Quantification and Global Sensitivity Analysis for Transient Wave Propagation in Pressurized Pipes

Uncertainty Quantification and Global Sensitivity Analysis for Transient Wave Propagation in Pressurized Pipes

Influence of the mechanical and geometrical parameters on the cellular uptake of nanoparticles: A stochastic approach

A Multi-Scale Attention Network for Uncertainty Analysis of Ground Penetrating Radar Modeling

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