A multiscale computational framework using active learning to model complex suspension flows

Chang, Yu-Jen; Huang, Hsuan-Yu; Chern, Ruey-Lin; Chou, Yi-Ju

doi:10.1016/j.jcp.2023.112481

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Cited by 4 publications

References 79 publications

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Data-driven techniques in rheology: Developments, challenges and perspective

Mangal,

Jha,

Dabiri

et al. 2025

Current Opinion in Colloid & Interface Science

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Data-driven techniques in rheology: Developments, challenges and perspective

Mangal,

Jha,

Dabiri

et al. 2025

Current Opinion in Colloid & Interface Science

View full text Add to dashboard Cite

Multiscale data-driven modeling of transient deposition physics of dense granular flows

Nikooei,

Edward Choi,

Zhang

2025

Computers and Geotechnics

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Short Review on Machine Learning-Based Multi-Scale Simulation in Rheology

Miyamoto

2024

J. Soc. Rheol., Jpn

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We briefly review the machine-learning (ML) applications for rheological research, particularly on the multi-scale simulation (MSS) techniques for complex fluid flows. For such simulations, it is essential to accurately model the constitutive relation (i.e., the strain-rate and stress relation) of complex fluids. Several past studies have found that ML applications for modeling the constitutive relation can reasonably accelerate the flow predictions from a microscopically resolved description of complex fluids such as polymers and colloidal suspensions. Nevertheless, even when the proposed regression models are consistent with the physical knowledge, several outstanding questions remain to be answered, for example, how to ensure the robustness of the predictions. This review summarizes the methods to obtain constitutive models using ML techniques and those applications based on molecular and phenomenological knowledge. Furthermore, we provide a perspective on how to develop the ML framework starting from a microscopic description.

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A multiscale computational framework using active learning to model complex suspension flows

Cited by 4 publications

References 79 publications

Data-driven techniques in rheology: Developments, challenges and perspective

Data-driven techniques in rheology: Developments, challenges and perspective

Multiscale data-driven modeling of transient deposition physics of dense granular flows

Short Review on Machine Learning-Based Multi-Scale Simulation in Rheology

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