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Establishing a Competing Risk Regression Nomogram Model for Survival Data

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Published on: October 23, 2020

Accelerating multiscale modelling of fluids with on-the-fly Gaussian process regression.

David Stephenson¹, James R Kermode², Duncan A Lockerby¹

¹1School of Engineering, University of Warwick, Coventry, CV4 7AL UK.

Microfluidics and Nanofluidics

|April 2, 2019

Summary

This study introduces a new method using Gaussian process regression to speed up complex fluid simulations. It accurately predicts atomic behavior, reducing the need for extensive molecular dynamics simulations and improving computational efficiency.

Keywords:

Hybrid methods Machine learning Micro/nanofluidics Molecular dynamics Multiscale modelling

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Area of Science:

Multiscale modeling
Computational fluid dynamics
Statistical learning

Background:

Hybrid continuum-atomistic models are crucial for simulating fluid systems at multiple scales.
Molecular dynamics (MD) simulations are computationally expensive, limiting their application in large-scale systems.
Existing hybrid methods often require redundant atomistic simulations, leading to inefficiencies.

Purpose of the Study:

To develop an accelerated hybrid model for multiscale fluidic systems.
To leverage Gaussian process regression (GPR) as a surrogate for MD simulations.
To enable on-the-fly uncertainty quantification and adaptive database augmentation.

Main Methods:

Gaussian Process Regression (GPR) was employed as a surrogate model for MD simulations.
The GPR model predicts atomic-scale information from continuum model inputs and outputs.
An adaptive strategy was implemented to perform new MD simulations when prediction uncertainty exceeds a threshold.

Main Results:

The hybrid scheme accurately predicts atomic-scale information, achieving accuracy near thermal noise levels for low uncertainty thresholds.
Significant computational speed-ups (up to an order of magnitude) were achieved compared to full MD simulations.
The trade-off between accuracy and computational efficiency can be tuned by adjusting the uncertainty threshold.

Conclusions:

The proposed GPR-based hybrid scheme offers a substantial improvement in efficiency for multiscale fluidic simulations.
This method reduces the reliance on numerous, often repetitive, MD simulations.
The tunable accuracy-efficiency balance makes the scheme adaptable to various research needs in fluid dynamics.