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

Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

222
Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence...
222
Multicompartment Models: Overview01:14

Multicompartment Models: Overview

324
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.
These models offer a more comprehensive representation of drug behavior in the body than one-compartment models. They accommodate the complexity of drug distribution,...
324
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

152
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...
152
Classification of Systems-I01:26

Classification of Systems-I

379
Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:
379

You might also read

Related Articles

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

Sort by
Same author

Open-Source Benchmarking of Plant-Based and Animal Meats.

Foods (Basel, Switzerland)·2026
Same author

Generative artificial intelligence creates delicious, sustainable, and nutritious burgers.

NPJ science of food·2026
Same author

Texture Independently Drives Liking in AI-Generated Alternative Protein Burgers.

Foods (Basel, Switzerland)·2026
Same author

Predictive modeling in biology and medicine: Digital twins and multi-scale modeling.

PLoS computational biology·2026
Same author

Mechanical, rheological, and sensory characterization of lion's mane mushroom steak.

Current research in food science·2026
Same author

Neuronal silence as a predictive biomarker and target for epileptic seizures suppression.

Scientific reports·2026
Same journal

Computational Intelligence in Stochastic Reconstruction of Porous Microstructures for Image-Based Poro/Micro-Mechanical Modeling.

Archives of computational methods in engineering : state of the art reviews·2026
Same journal

A review of recent advances in data-driven computer vision methods for structural damage evaluation: algorithms, applications, challenges, and future opportunities.

Archives of computational methods in engineering : state of the art reviews·2025
Same journal

A Scoping Review on Simulation-Based Design Optimization in Marine Engineering: Trends, Best Practices, and Gaps.

Archives of computational methods in engineering : state of the art reviews·2024
Same journal

Artificial Intelligence in Physical Sciences: Symbolic Regression Trends and Perspectives.

Archives of computational methods in engineering : state of the art reviews·2023
Same journal

Recent Advances in Machine Learning-Based Models for Prediction of Antiviral Peptides.

Archives of computational methods in engineering : state of the art reviews·2023
Same journal

A Comprehensive Analysis of Deep Learning-Based Approaches for Prediction and Prognosis of Infectious Diseases.

Archives of computational methods in engineering : state of the art reviews·2023
See all related articles

Related Experiment Video

Updated: Nov 3, 2025

Constructing and Visualizing Models using Mime-based Machine-learning Framework
06:19

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Published on: July 22, 2025

1.4K

Multiscale modeling meets machine learning: What can we learn?

Grace C Y Peng1, Mark Alber2, Adrian Buganza Tepole3

  • 1National Institutes of Health, Bethesda, Maryland, USA.

Archives of Computational Methods in Engineering : State of the Art Reviews
|June 7, 2021
PubMed
Summary
This summary is machine-generated.

Machine learning excels in diagnostics but struggles with sparse data prognosis. Combining machine learning with physics-based multiscale modeling offers robust solutions for complex biological systems.

Keywords:
Machine learningbiomedicinemultiscale modelingphysics-based simulation

More Related Videos

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
05:47

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

811
Author Spotlight: Integrated Multi-Omics Analysis for Unveiling Multicellular Immune Signatures in Clinical Heart Attack Cohorts
08:51

Author Spotlight: Integrated Multi-Omics Analysis for Unveiling Multicellular Immune Signatures in Clinical Heart Attack Cohorts

Published on: September 20, 2024

1.7K

Related Experiment Videos

Last Updated: Nov 3, 2025

Constructing and Visualizing Models using Mime-based Machine-learning Framework
06:19

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Published on: July 22, 2025

1.4K
Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
05:47

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

811
Author Spotlight: Integrated Multi-Omics Analysis for Unveiling Multicellular Immune Signatures in Clinical Heart Attack Cohorts
08:51

Author Spotlight: Integrated Multi-Omics Analysis for Unveiling Multicellular Immune Signatures in Clinical Heart Attack Cohorts

Published on: September 20, 2024

1.7K

Area of Science:

  • Biomedical Sciences
  • Computational Mechanics
  • Life Sciences

Background:

  • Machine learning (ML) shows success in image recognition for diagnostics (e.g., radiology, pathology) due to large annotated datasets.
  • ML often underperforms in prognostic tasks, particularly with sparse or noisy data, where physics-based simulations remain crucial.
  • Classical simulation methods face challenges in bridging different biological scales and understanding emergent functions.

Purpose of the Study:

  • To review the synergistic integration of machine learning and multiscale modeling in biomedical sciences.
  • To identify areas where ML and multiscale modeling can mutually enhance each other's capabilities.
  • To discuss current applications, opportunities, challenges, and future directions for robust biological system modeling.

Main Methods:

  • Integrating physics-based knowledge (equations, constraints) into ML models to handle ill-posed problems and sparse data.
  • Employing ML within multiscale modeling frameworks to create surrogate models and identify system dynamics.
  • Analyzing sensitivities and quantifying uncertainty to bridge scales and understand emergent functions.

Main Results:

  • ML can improve the robustness of models dealing with sparse and noisy biomedical data by incorporating physical laws.
  • Multiscale modeling benefits from ML for tasks like parameter estimation, sensitivity analysis, and uncertainty quantification.
  • The combination facilitates a deeper understanding of biological systems from molecular to organismal levels.

Conclusions:

  • The integration of machine learning and multiscale modeling presents a powerful approach for advancing biomedical research and precision medicine.
  • This synergy addresses limitations of individual methods, particularly in handling complex biological data and predictive modeling.
  • Fostering interdisciplinary collaboration is key to developing robust and efficient computational models for life sciences.