Using Gaussian Process Regression (GPR) models with the Matérn covariance function to predict the dynamic viscosity and torque of SiO<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" id="d1e1345" altimg="si4.svg"><mml:msub><mml:mrow/><mml:mrow><mml:mi mathvariant="bold">2</mml:mi></mml:mrow></mml:msub></mml:math>/Ethylene glycol nanofluid: A machine learning approach

Dai, X.; Andani, Hamid Taheri; Alizadeh, As’ad; Abed, Azher M.; Smaisim, Ghassan Fadhil; Hadrawi, Salema K.; Karimi, Maryam; Shamsborhan, Mahmoud; Toghraie, Davood

doi:10.1016/j.engappai.2023.106107

Cited by 38 publications

(1 citation statement)

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“…Properly tuning these hyperparameters is essential for the model's performance and for its ability to capture the underlying patterns in the data [28][29][30].…”

Section: Gaussian Process Regressionmentioning

confidence: 99%

Twofold Machine-Learning and Molecular Dynamics: A Computational Framework

Stavrogiannis,

Sofos,

Sagri

et al. 2023

Computers

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Data science and machine learning (ML) techniques are employed to shed light into the molecular mechanisms that affect fluid-transport properties at the nanoscale. Viscosity and thermal conductivity values of four basic monoatomic elements, namely, argon, krypton, nitrogen, and oxygen, are gathered from experimental and simulation data in the literature and constitute a primary database for further investigation. The data refers to a wide pressure–temperature (P-T) phase space, covering fluid states from gas to liquid and supercritical. The database is enriched with new simulation data extracted from our equilibrium molecular dynamics (MD) simulations. A machine learning (ML) framework with ensemble, classical, kernel-based, and stacked algorithmic techniques is also constructed to function in parallel with the MD model, trained by existing data and predicting the values of new phase space points. In terms of algorithmic performance, it is shown that the stacked and tree-based ML models have given the most accurate results for all elements and can be excellent choices for small to medium-sized datasets. In such a way, a twofold computational scheme is constructed, functioning as a computationally inexpensive route that achieves high accuracy, aiming to replace costly experiments and simulations, when feasible.

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“…Properly tuning these hyperparameters is essential for the model's performance and for its ability to capture the underlying patterns in the data [28][29][30].…”

Section: Gaussian Process Regressionmentioning

confidence: 99%