activity
20092023
most citedAnalyzing drop coalescence in microfluidic device with a deep learning generative model

14 citations · 48 across the 20 of their papers we have counts for

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Showing 2022Show all

5 papers · 1 filter

physics.flu-dyn2022

Drop encapsulation and bubble bursting in surfactant-laden flows in capillary channels

Paula Pico, Lyes Kahouadji, Seungwon Shin +3

We present a parametric study of the unsteady phenomena associated with the flow of elongated gas bubbles travelling through liquid-filled square capillaries under high Weber numbe…

physics.class-ph2022

Time-dependent modelling of thin poroelastic films drying on deformable plates

Matthew G. Hennessy, Richard V. Craster, Omar K. Matar

Understanding the generation of mechanical stress in drying, particle-laden films is important for a wide range of industrial processes. The cantilever experiment allows the stress…

cs.LG2022★ 9 cited

Generalised Latent Assimilation in Heterogeneous Reduced Spaces with Machine Learning Surrogate Models

Sibo Cheng, Jianhua Chen, Charitos Anastasiou +5

Reduced-order modelling and low-dimensional surrogate models generated using machine learning algorithms have been widely applied in high-dimensional dynamical systems to improve t…

physics.flu-dyn2022★ 4 cited

Conjugate heat transfer effects on flow boiling in microchannels

F. Municchi, I. El Mellas, O. K. Matar +1

This article presents a computational study of saturated flow boiling in non-circular microchannels. The unit channel of a multi-microchannel evaporator, consisting of the fluidic…

stat.ML2022★ 1 cited

Rule-based Evolutionary Bayesian Learning

Themistoklis Botsas, Lachlan R. Mason, Omar K. Matar +1

In our previous work, we introduced the rule-based Bayesian Regression, a methodology that leverages two concepts: (i) Bayesian inference, for the general framework and uncertainty…