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Physics-agnostic and physics-infused machine learning for thin films flows: modelling, and predictions from small data
Numerical simulations of multiphase flows are crucial in numerous engineering applications,
but are often limited by the computationally demanding solution of the Navier–Stokes (NS) …
but are often limited by the computationally demanding solution of the Navier–Stokes (NS) …
Sampling low-fidelity outputs for estimation of high-fidelity density and its tails
In a multifidelity setting, data are available under the same conditions from two (or more)
sources, eg, computer codes, one being lower-fidelity but computationally cheaper, and the …
sources, eg, computer codes, one being lower-fidelity but computationally cheaper, and the …
Super-resolution reconstruction of turbulence for Newtonian and viscoelastic fluids with a physical constraint
Super-resolution reconstruction (SR) of turbulent flow fields with high physical fidelity from
low-resolution turbulence data is a novel and cost-effective way in a turbulence study …
low-resolution turbulence data is a novel and cost-effective way in a turbulence study …
Comparison Between Linear and Hierarchical Multifidelity Models for Uncertainty Quantification in Turbulent Flows
Multifildelity models (MFMs) have received considerable attention in recent years for making
outer-loop problems in computational fluid dynamics (CFD) more affordable. A recent study …
outer-loop problems in computational fluid dynamics (CFD) more affordable. A recent study …
[PDF][PDF] Comparison between linear and non-linear multifidelity models for turbulent flow problems
This study compares two prominent multifidelity modelling approaches based on Gaussian
Process Regression (GPR): linear co-kriging method and a non-linear autoregressive GP …
Process Regression (GPR): linear co-kriging method and a non-linear autoregressive GP …
Rigorously Quantifying Uncertainties for Transport Phenomena in Molecular Simulations
Y Li - 2023 - search.proquest.com
The field of computational materials science faces various challenges in data processing,
including dealing with high-dimensional parameter spaces and error analysis. Uncertainty …
including dealing with high-dimensional parameter spaces and error analysis. Uncertainty …
Bayesian Optimisation of blowing and suction for drag reduction on a transonic airfoil
Wall-normal blowing and suction has shown to be a promising active control method for
friction drag reduction. In this work, we exploit a Bayesian optimization framework based on …
friction drag reduction. In this work, we exploit a Bayesian optimization framework based on …