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Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

79
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
79
Newtonian Fluid: Problem Solving01:18

Newtonian Fluid: Problem Solving

256
Newtonian fluids exhibit a constant viscosity, meaning their shear stress and shear strain rate are directly proportional. This property ensures a predictable and stable response to applied forces, maintaining a linear relationship between force and flow. Examples include water, air, and light oils, consistently demonstrating this proportional behavior regardless of external conditions.
A velocity gradient forms within the fluid when a Newtonian fluid is placed between two parallel plates, with...
256
One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation01:24

One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation

566
This lesson introduces two critical methods in pharmacokinetics, the Wagner-Nelson and Loo-Riegelman methods, used for estimating the absorption rate constant (ka) for drugs administered via non-intravenous routes. The Wagner-Nelson method relates ka to the plasma concentration derived from the slope of a semilog percent unabsorbed time plot. However, it is limited to drugs with one-compartment kinetics and can be impacted by factors like gastrointestinal motility or enzymatic degradation.
On...
566
Three-Dimensional Force System:Problem Solving01:30

Three-Dimensional Force System:Problem Solving

691
A three-dimensional force system refers to a scenario in which three forces act simultaneously in three different directions. This type of problem is commonly encountered in physics and engineering, where it is necessary to calculate the resultant force on the system, which can then be used to predict or analyze the behavior of the object or structure under consideration.
To solve a three-dimensional force system, first resolve each force into its respective scalar components. Do this using...
691
Dynamic Modulus of Elasticity of Concrete01:16

Dynamic Modulus of Elasticity of Concrete

382
The dynamic modulus of elasticity assesses how a concrete structure deforms under impact or dynamic loads. It is typically higher than the static modulus of elasticity, measured under slow, steady loading conditions.
The sonic test is a common method to determine the dynamic modulus. In this test, a concrete beam, sized either 6 x 6 x 30 inches or 4 x 4 x 20 inches, is clamped at its center. Vibrations are initiated at one end of the beam by an electromagnetic exciter unit powered by...
382
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

96
Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...
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Time-Concentration Superposition for Linear Viscoelasticity of Polymer Solutions.

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Related Experiment Video

Updated: Jul 16, 2025

Characterizing Dissipative Elastic Metamaterials Produced by Additive Manufacturing
09:39

Characterizing Dissipative Elastic Metamaterials Produced by Additive Manufacturing

Published on: June 28, 2024

958

A General Deep Learning Method for Computing Molecular Parameters of a Viscoelastic Constitutive Model by Solving an

Minghui Ye1, Yuan-Qi Fan1, Xue-Feng Yuan1

  • 1Institute for Systems Rheology, Guangzhou University, No. 230 West Outer Ring Road, Higher Education Mega-Center, Panyu District, Guangzhou 510006, China.

Polymers
|September 9, 2023
PubMed
Summary
This summary is machine-generated.

A new deep neural network (DNN) method accurately predicts molecular parameters from fluid viscoelastic properties. This approach offers robust solutions for complex fluid modeling and formulation design in research and industry.

Keywords:
constitutive equationdeep neural networkinverse problemmachine learningpolymeric fluidsviscoelasticity

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Area of Science:

  • Rheology
  • Polymer Physics
  • Computational Fluid Dynamics

Background:

  • Predicting molecular parameters from macroscopic viscoelastic properties is crucial for designing complex fluids.
  • Current methods face challenges in accuracy and robustness for diverse applications.

Purpose of the Study:

  • To develop a general deep learning method for computing molecular parameters of viscoelastic models by solving inverse problems.
  • To validate the accuracy, convergence, and robustness of a deep neural network (DNN)-based solver.

Main Methods:

  • A deep neural network (DNN) was employed as a numerical solver for inverse problems.
  • The Rolie-Poly model was used to simulate linear and non-linear rheometric properties of entangled polymer solutions.
  • The DNN solver's performance was tested against various concentrations and noise levels in stress data.

Main Results:

  • The DNN-based solver demonstrated rapid convergence and robustness against minor input data noise.
  • Accurate prediction of molecular parameters for monodisperse linear lambda DNA solutions was achieved across wide shear rates and concentrations.
  • The method successfully predicted power-law concentration scaling, closely matching experimental estimates.

Conclusions:

  • Deep neural networks provide an effective and efficient tool for solving inverse problems in rheology.
  • The validated DNN solver accurately computes molecular parameters, advancing molecular and formulation design for complex fluids.
  • This approach enhances the predictive capability for material functions based on viscoelastic properties.