Related Experiment Video

Updated: Jun 5, 2025

Analyzing Mitochondrial Morphology Through Simulation Supervised Learning
12:06

Analyzing Mitochondrial Morphology Through Simulation Supervised Learning

Published on: March 3, 2023

3.9K

Publisher Correction: Adaptive AI-based surrogate modelling via transfer learning for DEM simulation of

Ahmed Hadi1, Morteza Moradi2, Yusong Pang3

  • 1Department of Maritime and Transport Technology, Faculty of Mechanical Engineering, Delft University of Technology, Delft, 2628CD, The Netherlands. A.H.Hadi-1@tudelft.nl.

Scientific Reports
|December 5, 2024
PubMed
Abstract

No abstract available in PubMed .

More Related Videos

Surrogate Model Development for Digital Experiments in Welding09:17

Surrogate Model Development for Digital Experiments in Welding

Published on: March 28, 2025

702
Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches09:47

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

954

Related Concept Videos

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

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

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...
40
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
27