Jove
Visualize
Contact Us

Related Concept Videos

Typical Model Studies01:30

Typical Model Studies

415
Fluid mechanics model studies often utilize scaled-down systems to predict fluid behavior in full-scale environments, such as river flows, dam spillways, and structures interacting with open surfaces. Maintaining Froude number similarity in river models is crucial, as it replicates surface flow features like wave patterns and velocities.
415
Modeling and Similitude01:12

Modeling and Similitude

315
Scaled modeling is a fundamental technique in engineering, enabling the study of large and complex systems by creating smaller, manageable replicas that recreate critical characteristics of the original. In hydrology and civil infrastructure, for example, scaled models of dams help analyze water flow, turbulence, and pressure. This method allows for accurate predictions of real-world behavior within a controlled environment, significantly reducing the cost and time involved in full-scale...
315
Physiological Pharmacokinetic Models: Blood Flow-Limited Versus Diffusion-Limited Models00:57

Physiological Pharmacokinetic Models: Blood Flow-Limited Versus Diffusion-Limited Models

134
Physiological pharmacokinetic models, often called flow-limited or perfusion models, typically assume a swift drug distribution between tissue and venous blood, creating a rapid drug equilibrium. This premise is based on the idea that drug diffusion is extremely fast, and the cell membrane presents no barrier to drug permeation. In this scenario, where no drug binding occurs, the drug concentration in the tissue equals that of the venous blood leaving the tissue. This greatly simplifies the...
134
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

93
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...
93
Plane Potential Flows01:23

Plane Potential Flows

438
Plane potential flows simplify fluid motion by assuming the fluid to be irrotational and incompressible. These characteristics allow these flows to be described by a velocity potential function, ϕ, representing the flow speed in a given direction, and a stream function, ψ, that visualizes the flow path, both governed by Laplace's equation. These parameters help in estimating flow patterns, velocity distributions, and pressure fields around various hydraulic structures.
Uniform...
438
Estimation of the Physical Quantities01:05

Estimation of the Physical Quantities

4.6K
On many occasions, physicists, other scientists, and engineers need to make estimates of a particular quantity. These are sometimes referred to as guesstimates, order-of-magnitude approximations, back-of-the-envelope calculations, or Fermi calculations. The physicist Enrico Fermi was famous for his ability to estimate various kinds of data with surprising precision. Estimating does not mean guessing a number or a formula at random. Instead, estimation means using prior experience and sound...
4.6K

You might also read

Related Articles

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

Sort by
Same author

Sub-2 nm Equivalent-Oxide-Thickness Ferroelectric Transistors for Cryogenic Memory and Computing.

ACS nano·2026
Same author

Interpreting artificial neural network-based modeling of 4 H-SiC mosfets using explainable AI.

Scientific reports·2026
Same author

Exploring Chemical Space with Chemistry-Inspired Dynamic Quantum Circuits in the NISQ Era.

Journal of chemical theory and computation·2025
Same author

Predictions of Lattice Parameters in NiTi High-Entropy Shape-Memory Alloys Using Different Machine Learning Models.

Materials (Basel, Switzerland)·2024
Same author

LYNSU: automated 3D neuropil segmentation of fluorescent images for <i>Drosophila</i> brains.

Frontiers in neuroinformatics·2024
Same author

Using U-Net convolutional neural network to model pixel-based electrostatic potential distributions in GaN power MIS-HEMTs.

Scientific reports·2024
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 Experiment Video

Updated: Aug 14, 2025

Visualizing Hyporheic Flow Through Bedforms Using Dye Experiments and Simulation
09:49

Visualizing Hyporheic Flow Through Bedforms Using Dye Experiments and Simulation

Published on: November 18, 2015

12.3K

Device simulations with A U-Net model predicting physical quantities in two-dimensional landscapes.

Wen-Jay Lee1, Wu-Tsung Hsieh2, Bin-Horn Fang2

  • 1National Center for High-performance Computing, Hsinchu, Taiwan (R.O.C.).

Scientific Reports
|January 13, 2023
PubMed
Summary

Machine learning models predict physical quantities in MOSFET simulations, significantly reducing computation time. This approach offers a faster, interpretable alternative to traditional methods for device design.

More Related Videos

Author Spotlight: Understanding Riverine Nitrogen Impacts and Primary Productivity for Effective Nutrient Management
05:04

Author Spotlight: Understanding Riverine Nitrogen Impacts and Primary Productivity for Effective Nutrient Management

Published on: July 14, 2023

456
Exploring the Effects of Atmospheric Forcings on Evaporation: Experimental Integration of the Atmospheric Boundary Layer and Shallow Subsurface
13:27

Exploring the Effects of Atmospheric Forcings on Evaporation: Experimental Integration of the Atmospheric Boundary Layer and Shallow Subsurface

Published on: June 8, 2015

8.8K

Related Experiment Videos

Last Updated: Aug 14, 2025

Visualizing Hyporheic Flow Through Bedforms Using Dye Experiments and Simulation
09:49

Visualizing Hyporheic Flow Through Bedforms Using Dye Experiments and Simulation

Published on: November 18, 2015

12.3K
Author Spotlight: Understanding Riverine Nitrogen Impacts and Primary Productivity for Effective Nutrient Management
05:04

Author Spotlight: Understanding Riverine Nitrogen Impacts and Primary Productivity for Effective Nutrient Management

Published on: July 14, 2023

456
Exploring the Effects of Atmospheric Forcings on Evaporation: Experimental Integration of the Atmospheric Boundary Layer and Shallow Subsurface
13:27

Exploring the Effects of Atmospheric Forcings on Evaporation: Experimental Integration of the Atmospheric Boundary Layer and Shallow Subsurface

Published on: June 8, 2015

8.8K

Area of Science:

  • Semiconductor device physics
  • Computational materials science
  • Machine learning applications

Background:

  • Technology Computer-Aided Design (TCAD) is crucial for cost-effective device design but faces challenges with complex structures.
  • Machine learning (ML) accelerates device simulations and enables inverse design, yet predicting physical quantities remains difficult.
  • Traditional self-consistent calculations for essential physical quantities are time-consuming.

Purpose of the Study:

  • To develop and evaluate ML models for predicting physical quantities in MOSFET devices.
  • To demonstrate the feasibility of using convolutional neural networks for interpretable device simulations.
  • To compare the speed and accuracy of ML-based predictions against traditional methods.

Main Methods:

  • Employed a modified U-Net architecture for predicting 2D physical quantities.
  • Trained ML models on MOSFET device data.
  • Analyzed prediction errors to understand model limitations and data requirements.
  • Compared computation times with traditional self-consistent calculations.

Main Results:

  • Successfully predicted physical quantities (e.g., electric field, potential) for MOSFETs using U-Net models.
  • Demonstrated that prediction accuracy is dependent on the volume of training data.
  • Achieved significantly faster computation times for landscape predictions compared to traditional methods.
  • Highlighted the potential for interpretable ML in device simulations.

Conclusions:

  • The developed U-Net models offer a computationally efficient method for predicting physical quantities in semiconductor devices.
  • This work establishes a foundation for interpretable ML-driven device simulations.
  • The findings suggest ML can accelerate the design and analysis of complex semiconductor devices.