Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

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

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

107
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...
107
Parameters Affecting Nonlinear Elimination: Zero-Order Input, First-Order Absorption and Two-Compartment Model01:13

Parameters Affecting Nonlinear Elimination: Zero-Order Input, First-Order Absorption and Two-Compartment Model

129
Drugs administered through various routes can lead to nonlinear elimination, resulting in complex pharmacokinetic behaviors crucial to understanding efficacious drug dosing.
When a drug is administered through a constant intravenous infusion and eliminated via nonlinear pharmacokinetics, it follows zero-order input. For example, oral drugs undergo first-order absorption upon administration and are eliminated through nonlinear pharmacokinetics.
In the case of subcutaneously administered drugs,...
129
Linear Approximation in Time Domain01:21

Linear Approximation in Time Domain

130
Nonlinear systems often require sophisticated approaches for accurate modeling and analysis, with state-space representation being particularly effective. This method is especially useful for systems where variables and parameters vary with time or operating conditions, such as in a simple pendulum or a translational mechanical system with nonlinear springs.
For a simple pendulum with a mass evenly distributed along its length and the center of mass located at half the pendulum's length,...
130
PD Controller: Design01:26

PD Controller: Design

360
In automotive engineering, car suspension systems often employ Proportional Derivative (PD) controllers to enhance performance. PD controllers are utilized to adjust the damping force in response to road conditions. A controller, acting as an amplifier with a constant gain, demonstrates proportional control, with output directly mirroring input.
Designing a continuous-data controller requires selecting and linking components like adders and integrators, which are fundamental in Proportional,...
360
Principle of Linear Impulse and Momentum for a Single Particle: Problem Solving01:23

Principle of Linear Impulse and Momentum for a Single Particle: Problem Solving

614
Consider a wooden box and a cylinder of known masses m1 and m2, respectively,  hanging from a ceiling with the help of a massless pulley system.
614
Pharmacokinetic Models: Comparison and Selection Criterion01:26

Pharmacokinetic Models: Comparison and Selection Criterion

155
Physiological and compartmental models are valuable tools used in studying biological systems. These models rely on differential equations to maintain mass balance within the system, ensuring an accurate representation of the dynamic processes at play.
Physiological models take a detailed approach by considering specific molecular processes. They can predict drug distribution, metabolism, and elimination changes, providing a comprehensive understanding of how drugs interact with the body.
155

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Phase Space Reconstruction from Accelerator Beam Measurements Using Neural Networks and Differentiable Simulations.

Physical review letters·2023
Same author

Association of Serum Ferritin Levels with Microalbuminuria, Glycemic Control and Dyslipidemia.

The Journal of the Association of Physicians of India·2022
Same author

Design of a prospective, longitudinal cohort of people living with type 1 diabetes exploring factors associated with the residual cardiovascular risk and other diabetes-related complications: The SFDT1 study.

Diabetes & metabolism·2021
Same author

Recovering the phase and amplitude of X-ray FEL pulses using neural networks and differentiable models.

Optics express·2021
Same author

Invasive and in situ squamous cell carcinoma of the skin: a nationwide study in Iceland.

The British journal of dermatology·2021
Same author

COVID-19 symptoms masking inaugural ketoacidosis of type 1 diabetes.

Diabetes & metabolism·2020

Related Experiment Video

Updated: Sep 21, 2025

A Modeling and Simulation Method for Preliminary Design of an Electro-Variable Displacement Pump
09:04

A Modeling and Simulation Method for Preliminary Design of an Electro-Variable Displacement Pump

Published on: June 1, 2022

3.2K

Differentiable Preisach Modeling for Characterization and Optimization of Particle Accelerator Systems with

R Roussel1, A Edelen1, D Ratner1

  • 1SLAC National Accelerator Laboratory, Menlo Park, California 94025, USA.

Physical Review Letters
|June 3, 2022
PubMed
Summary

This study introduces a novel machine learning approach to model hysteresis in particle accelerators, improving control and precision. This method accurately characterizes magnetic hysteresis using beam-based measurements, overcoming limitations of traditional models.

More Related Videos

Author Spotlight: Optimization of Airflow Velocities in Battery Cooling Systems for Enhanced Thermal Performance and Reduced Energy Consumption
10:36

Author Spotlight: Optimization of Airflow Velocities in Battery Cooling Systems for Enhanced Thermal Performance and Reduced Energy Consumption

Published on: November 3, 2023

1.7K
Setting Limits on Supersymmetry Using Simplified Models
07:46

Setting Limits on Supersymmetry Using Simplified Models

Published on: November 15, 2013

8.7K

Related Experiment Videos

Last Updated: Sep 21, 2025

A Modeling and Simulation Method for Preliminary Design of an Electro-Variable Displacement Pump
09:04

A Modeling and Simulation Method for Preliminary Design of an Electro-Variable Displacement Pump

Published on: June 1, 2022

3.2K
Author Spotlight: Optimization of Airflow Velocities in Battery Cooling Systems for Enhanced Thermal Performance and Reduced Energy Consumption
10:36

Author Spotlight: Optimization of Airflow Velocities in Battery Cooling Systems for Enhanced Thermal Performance and Reduced Energy Consumption

Published on: November 3, 2023

1.7K
Setting Limits on Supersymmetry Using Simplified Models
07:46

Setting Limits on Supersymmetry Using Simplified Models

Published on: November 15, 2013

8.7K

Area of Science:

  • Accelerator physics
  • Machine learning
  • Nonlinear dynamics

Background:

  • Accurate online modeling is crucial for advancing particle accelerator performance.
  • Hysteresis effects in accelerator components are often overlooked but cause significant reproducibility errors in high-precision systems.
  • Existing models struggle to capture complex hysteresis phenomena.

Purpose of the Study:

  • To develop a high-fidelity, nonparametric model for systems exhibiting hysteresis using machine learning.
  • To experimentally demonstrate the in-situ characterization of magnetic hysteresis in accelerator magnets.
  • To improve accelerator optimization by accounting for hysteresis effects.

Main Methods:

  • Combined the classical Preisach model of hysteresis with machine learning techniques.
  • Developed nonparametric, high-fidelity models for arbitrary hysteretic systems.
  • Integrated a hysteresis model with a Bayesian statistical model for beam response characterization.

Main Results:

  • Successfully created efficient, high-fidelity models of hysteresis.
  • Demonstrated in-situ characterization of magnetic hysteresis using only beam-based measurements.
  • Showcased how joint hysteresis-Bayesian models overcome optimization limitations.

Conclusions:

  • Machine learning combined with Preisach models offers a powerful tool for modeling hysteresis in accelerators.
  • In-situ beam-based characterization of magnetic hysteresis is feasible and effective.
  • Accounting for hysteresis is essential for enhancing the performance and precision of future particle accelerators.