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Related Concept Videos

Multicompartment Models: Overview01:14

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Multicompartment models are mathematical constructs that depict how drugs are distributed and eliminated within the body. They segment the body into several compartments, symbolizing various physiological or anatomical areas connected through drug transfer processes such as absorption, metabolism, distribution, and elimination.
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Mechanistic models, a category encompassing both physiological and compartmental modeling, differ from empirical models' approaches to incorporating known factors about the systems being modeled. Empirical models describe data with minimal assumptions, while mechanistic models aim to provide a robust description of available data by specifying assumptions and integrating known factors about the system. Compartmental analysis is a key example of a mechanistic model in pharmacokinetics and...
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Multiscale Sampling of a Heterogeneous Water/Metal Catalyst Interface using Density Functional Theory and Force-Field Molecular Dynamics
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Multifidelity computing for coupling full and reduced order models.

Shady E Ahmed1, Omer San1, Kursat Kara1

  • 1School of Mechanical & Aerospace Engineering, Oklahoma State University, Stillwater, OK, United States of America.

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Summary
This summary is machine-generated.

This study introduces a hybrid physics-machine learning approach to simulate complex transport processes. A novel interface learning method effectively bridges high-fidelity and reduced-order models for enhanced digital twin technologies.

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Area of Science:

  • Computational Science
  • Machine Learning Applications
  • Multiphysics Simulations

Background:

  • Hybrid physics-machine learning models are crucial for simulating complex transport processes.
  • Multiphysics systems often involve multiple spatiotemporal scales and multifidelity formulations.
  • Integrating diverse computational entities presents a significant challenge in scientific and engineering applications.

Purpose of the Study:

  • To develop a robust hybrid analysis and modeling approach for integrated mixed-fidelity simulations.
  • To create building blocks for predictive digital twin technologies by combining full-order and reduced-order models.
  • To introduce an interface learning method for effective coupling of high and low-fidelity models.

Main Methods:

  • A physics-based full-order model (FOM) was combined with a data-driven reduced-order model (ROM).
  • A long short-term memory (LSTM) network was employed at the interface to bridge FOM and ROM.
  • The approach was tested on nonlinear advection-diffusion flow problems in a bifidelity setup.

Main Results:

  • The proposed interface learning effectively corrects or prolongs information between mixed-fidelity models.
  • The hybrid approach successfully addresses ROM-FOM coupling challenges in transport process simulations.
  • The method demonstrates a new way to handle interfacial errors in complex simulations.

Conclusions:

  • Hybrid physics-machine learning models offer a powerful framework for advanced simulations.
  • LSTM networks provide an effective mechanism for interfacing different fidelity models.
  • This integrated approach advances the development of predictive digital twin technologies for transport processes.