Machine learning acceleration of simulations of Stokesian suspensions

Kabacaoglu, Gokberk; Biros, George

doi:10.1103/physreve.99.063313

Cited by 4 publications

(1 citation statement)

References 32 publications

(63 reference statements)

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“…ML was also used to integrate images of blood flow to underlying physical laws to infer the flow field in microaneurysm [59]. Additionally, combined ML and high-fidelity simulation was considered to study the flow of vesicle suspension to reduce expensive computation [60].…”

Section: Introductionmentioning

confidence: 99%

Application of machine learning in predicting blood flow and red cell distribution in capillary vessel networks

Ebrahimi

Bagchi

2022

J. R. Soc. Interface.

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Capillary blood vessels in the body partake in the exchange of gas and nutrients with tissues. They are interconnected via multiple vascular junctions forming the microvascular network. Distributions of blood flow and red cells (RBCs) in such networks are spatially uneven and vary in time. Since they dictate the pathophysiology of tissues, their knowledge is important. Theoretical models used to obtain flow and RBC distribution in large networks have limitations as they treat each vessel as a one-dimensional segment and do not explicitly consider cell–cell and cell–vessel interactions. High-fidelity computational models that accurately model each individual RBC are computationally too expensive to predict haemodynamics in large vascular networks and over a long time. Here we investigate the applicability of machine learning (ML) techniques to predict blood flow and RBC distributions in physiologically realistic vascular networks. We acquire data from high-fidelity simulations of deformable RBC suspension flowing in the networks. With the flow and haematocrit specified at an inlet of vasculature, the ML models predict the time-averaged flow rate and RBC distributions in the entire network, time-dependent flow rate and haematocrit in each vessel and vascular bifurcation in isolation over a long time, and finally, simultaneous spatially and temporally evolving quantities through the vessel hierarchy in the networks.

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Section: Introductionmentioning

confidence: 99%

Application of machine learning in predicting blood flow and red cell distribution in capillary vessel networks

Ebrahimi

Bagchi

2022

J. R. Soc. Interface.

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Deep learning for solution and inversion of structural mechanics and vibrations

Haghighat

Bekar

Madenci

et al. 2021

Modeling and Computation in Vibration Problems, Volume 2

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Uncertainty quantification in coupled wildfire–atmosphere simulations at scale

Schwerdtner,

Law,

Wang

et al. 2024

PNAS Nexus

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Uncertainties in wildfire simulations pose a major challenge for making decisions about fire management, mitigation, and evacuations. However, ensemble calculations to quantify uncertainties are prohibitively expensive with high-fidelity models that are needed to capture today's ever more intense and severe wildfires. This work shows that surrogate models trained on related data enable scaling multi-fidelity uncertainty quantification to high-fidelity wildfire simulations of unprecedented scale with billions of degrees of freedom. The key insight is that correlation is all that matters while bias is irrelevant for speeding up uncertainty quantification when surrogate models are combined with high-fidelity models in multi-fidelity approaches. This allows the surrogate models to be trained on abundantly available or cheaply generated related data samples that can be strongly biased as long as they are correlated to predictions of high-fidelity simulations. Numerical results with scenarios of the Tubbs 2017 wildfire demonstrate that surrogate models trained on related data make multi-fidelity uncertainty quantification in large-scale wildfire simulations practical by reducing the training time by several orders of magnitude from three months to under three hours and predicting the burned area at least twice as accurately compared to using high-fidelity simulations alone for a fixed computational budget. More generally, the results suggest that leveraging related data can greatly extend the scope of surrogate modeling, potentially benefiting other fields that require uncertainty quantification in computationally expensive high-fidelity simulations.

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Machine learning acceleration of simulations of Stokesian suspensions

Cited by 4 publications

References 32 publications

Application of machine learning in predicting blood flow and red cell distribution in capillary vessel networks

Application of machine learning in predicting blood flow and red cell distribution in capillary vessel networks

Deep learning for solution and inversion of structural mechanics and vibrations

Uncertainty quantification in coupled wildfire–atmosphere simulations at scale

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