Computational modeling of flow-mediated angiogenesis: Stokes–Darcy flow on a growing vessel network

Srinivasan, Adithya; Moure, Adrian; Gomez, Hector

doi:10.1007/s00366-023-01889-6

Engineering with Computers

2023

DOI: 10.1007/s00366-023-01889-6

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Computational modeling of flow-mediated angiogenesis: Stokes–Darcy flow on a growing vessel network

Adithya Srinivasan,

Adrian Moure,

Hector Gomez

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2024

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Cited by 2 publications

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A deep neural network for operator learning enhanced by attention and gating mechanisms for long-time forecasting of tumor growth

Chen,

Li,

Zheng

2024

Engineering with Computers

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A deep neural network for operator learning enhanced by attention and gating mechanisms for long-time forecasting of tumor growth

Chen,

Li,

Zheng

2024

Engineering with Computers

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Efficient Numerical Implementation of the Time-Fractional Stochastic Stokes–Darcy Model

Baishemirov,

Berdyshev,

Baigereyev

et al. 2024

Fractal Fract

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This paper presents an efficient numerical method for the fractional-order generalization of the stochastic Stokes–Darcy model, which finds application in various engineering, biomedical and environmental problems involving interaction between free fluid flow and flows in porous media. Unlike the classical model, this model allows taking into account the hereditary properties of the process under uncertainty conditions. The proposed numerical method is based on the combined use of the sparse grid stochastic collocation method, finite element/finite difference discretization, a fast numerical algorithm for computing the Caputo fractional derivative, and a cost-effective ensemble strategy. The hydraulic conductivity tensor is assumed to be uncertain in this problem, which is modeled by the reduced Karhunen–Loève expansion. The stability and convergence of the deterministic numerical method have been rigorously proved and validated by numerical tests. Utilizing the ensemble strategy allowed us to solve the deterministic problem once for all samples of the hydraulic conductivity tensor, rather than solving it separately for each sample. The use of the algorithm for computing the fractional derivatives significantly reduced both computational cost and memory usage. This study also analyzes the influence of fractional derivatives on the fluid flow process within the fractional-order Stokes–Darcy model under uncertainty conditions.

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Computational modeling of flow-mediated angiogenesis: Stokes–Darcy flow on a growing vessel network

Cited by 2 publications

References 54 publications

A deep neural network for operator learning enhanced by attention and gating mechanisms for long-time forecasting of tumor growth

A deep neural network for operator learning enhanced by attention and gating mechanisms for long-time forecasting of tumor growth

Efficient Numerical Implementation of the Time-Fractional Stochastic Stokes–Darcy Model

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