Select Applications of Bayesian Data Analysis and Machine Learning to Flow Problems

Seryo, Naoki; Molina, John J.; Taniguchi, Takashi

doi:10.1678/rheology.49.97

Cited by 6 publications

(2 citation statements)

References 31 publications

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“…For example, by developing an extended version of the Stokesian dynamics embedded GP for Newtonian fluids. 43,44) When we do not know the constitutive equation or its parameters, we face the inverse problem of inferring the material functions. Given minimal rheological knowledge, ML method can also be used to infer portable and interpretable material functions.…”

Section: For Rheologymentioning

confidence: 99%

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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Section: For Rheologymentioning

confidence: 99%

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

Miyamoto

2024

J. Soc. Rheol., Jpn

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“…These studies have employed neural networks (NN), including deep NN [24], graph NN [25], recurrent NN [26], physics-informed NN [27][28][29], multi-fidelity NN [30], and tensor basis NN [31]. Gaussian processes (GP) have also been employed, for example, for strain-rate dependent viscosity [32] or for viscoelastic properties [33][34][35][36].…”

Section: Introductionmentioning

confidence: 99%

Response: Naoko Saito, Finding as Founding: Rejoinder to René Arcilla’s Review, Naoko Saito, Associate Professor of Education at Kyoto University, Japan. Kyoto University, Yoshidahonmachi, Sakyo-ku, Kyoto 606-8501

Saito

2020

Stud Philos Educ

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In , arXiv:1909), I reported on a double outburst and rebrightenings in 2018 in V544 Her. Such a phenomenon is usually observed in WZ Sge stars which evolved after the period bounce and the colors of V544 Her in quiescence apparently exclude this possibility. Although this phenomenon was considered to be rare, I detected almost exactly the same one in 2021 using ZTF, ATLAS and ASAS-SN public data. I also detected a phenomenon very similar to this in ASASSN-19yt in 2022. The same object showed a different type of outburst in 2019 whose morphology looked like that of an SS Cyg star. If ASASSN-19yt is an SU UMa star, the morphology of the 2019 outburst would challenge our knowledge in SU UMa stars. If this object, or V544 Her, is an SS Cyg star, what causes a double outburst and rebrightenings would become an unsolved problem in dwarf novae.

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Stokesian processes : inferring Stokes flows using physics-informed Gaussian processes

Molina,

Ogawa,

Taniguchi

2023

Mach. Learn.: Sci. Technol.

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We develop a probabilistic Stokes flow framework, using Physics Informed Gaussian Processes, which can be used to solve both forward/inverse flow problems with missing and/or noisy data. The physics of the problem, specified by the Stokes and continuity equations, is exactly encoded into the inference framework. Crucially, this means that we do not need to explicitly solve the Poisson equation for the pressure field, as a physically meaningful (divergence-free) velocity field will automatically be selected. We test our method on a simple pressure driven flow problem, i.e., flow through a sinusoidal channel, and compare against standard numerical methods (Finite Element and Direct Numerical Simulations). We obtain excellent agreement, even when solving inverse problems given only sub-sampled velocity data on low dimensional sub-spaces (i.e., 1 component of the velocity on $1D$ domains to reconstruct $2D$ flows). The proposed method will be a valuable tool for analyzing experimental data, where noisy/missing data is the norm.

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Select Applications of Bayesian Data Analysis and Machine Learning to Flow Problems

Cited by 6 publications

References 31 publications

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

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

Response: Naoko Saito, Finding as Founding: Rejoinder to René Arcilla’s Review, Naoko Saito, Associate Professor of Education at Kyoto University, Japan. Kyoto University, Yoshidahonmachi, Sakyo-ku, Kyoto 606-8501

Stokesian processes : inferring Stokes flows using physics-informed Gaussian processes

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