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

201
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...
201
Application of Nonlinear Inequalities01:29

Application of Nonlinear Inequalities

122
A nonlinear inequality describes a comparison involving an expression that curves or behaves more complexly than a straight line. These inequalities often appear in forms that include squares, products, or variables in the denominator.To solve such an inequality, one starts by rewriting it so that zero appears on one side. For example, the inequality:  can be factored as: This form makes it easier to identify the values that cause the expression to equal zero. In this case, the...
122
Gaussian Elimination: Problem Solving01:30

Gaussian Elimination: Problem Solving

90
Systems of linear equations in several variables are pivotal in modeling complex scenarios involving multiple unknowns and constraints. Such systems are widely used in various fields to represent relationships where several conditions must be simultaneously satisfied. Each variable in the system corresponds to an unknown quantity, while each equation imposes a linear constraint, leading to a structured approach for analyzing and solving real-world problems.A system of three equations with three...
90
Fast Decoupled and DC Powerflow01:24

Fast Decoupled and DC Powerflow

625
The fast decoupled power flow method addresses contingencies in power system operations, such as generator outages or transmission line failures. This method provides quick power flow solutions, essential for real-time system adjustments. Fast decoupled power flow algorithms simplify the Jacobian matrix by neglecting certain elements, leading to two sets of decoupled equations:
625
Systems of Equations01:25

Systems of Equations

99
A system of equations consists of multiple equations involving common variables. The objective is to identify values that simultaneously satisfy all equations. Systems of equations provide a framework for analyzing multiple constraints or relationships within a single problem context.Three primary algebraic techniques are used to solve systems: substitution, elimination, and graphical methods. The substitution method involves solving one equation for one variable and substituting the result...
99
Transmission-Line Differential Equations01:26

Transmission-Line Differential Equations

850
Transmission lines are essential components of electrical power systems. They are characterized by the distributed nature of resistance (R), inductance (L), and capacitance (C) per unit length. To analyze these lines, differential equations are employed to model the variations in voltage and current along the line.
Line Section Model
A circuit representing a line section of length Δx helps in understanding the transmission line parameters. The voltage V(x) and current i(x) are measured from...
850

You might also read

Related Articles

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

Sort by
Same author

Data-Driven Chance Constrained Mixed Integer Nonlinear Bilevel Optimization via Copulas.

Industrial & engineering chemistry research·2026
Same author

Hyperparameter Optimization of Non-linear Machine Learning Models Using Bi-level Data-Driven Optimization.

Computers & chemical engineering·2026
Same author

Learning Approximate Symbolic Solutions to Burgers' Equation using Symbolic Regression.

IFAC-PapersOnLine·2025
Same author

Selection of Fitness Criteria for Learning Interpretable PDE Solutions via Symbolic Regression.

Systems & control transactions·2025
Same author

Data-Driven Chance-Constrained Mixed Integer Nonlinear Bi-level Optimisation Via Copulas: Application To Integrated Planning And Scheduling Problems.

Systems & control transactions·2025
Same author

Planning Strategies in the Energy Sector: Integrating Bayesian Neural Networks and Uncertainty Quantification in Scenario Analysis & Optimization.

Computers & chemical engineering·2025
Same journal

scChat: A Large Language Model-Powered Co-Pilot for Contextualized Single-Cell RNA Sequencing Analysis.

AIChE journal. American Institute of Chemical Engineers·2026
Same journal

Spheres, tears, and spears: Regulating the perimeter and circularity of millimeter-sized alginate hydrogel beads.

AIChE journal. American Institute of Chemical Engineers·2026
Same journal

Internal control of brain networks via sparse feedback.

AIChE journal. American Institute of Chemical Engineers·2025
Same journal

Differential Effects of Confinement-Induced ROS Accumulation on Highly Motile Cancerous and Non-Cancerous Cells.

AIChE journal. American Institute of Chemical Engineers·2025
Same journal

Nanoparticle transport in biomimetic polymer-linked emulsions.

AIChE journal. American Institute of Chemical Engineers·2025
Same journal

Multifunctional Porous Soft Composites for Bimodal Wearable Cardiac Monitors.

AIChE journal. American Institute of Chemical Engineers·2024
See all related articles

Related Experiment Video

Updated: Dec 9, 2025

Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator
06:45

Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator

Published on: October 28, 2022

2.0K

A data-driven optimization algorithm for differential algebraic equations with numerical infeasibilities.

Burcu Beykal1,2, Melis Onel1,2, Onur Onel1,2,3

  • 1Artie McFerrin Department of Chemical Engineering, Texas A&M University, College Station, TX 77843, USA.

Aiche Journal. American Institute of Chemical Engineers
|September 14, 2020
PubMed
Summary
This summary is machine-generated.

This study introduces a Support Vector Machine (SVM) framework for optimizing Differential Algebraic Equations (DAEs). SVMs effectively identify numerical infeasibility boundaries, enabling data-driven optimization without full model discretization.

Keywords:
Data-Driven OptimizationDifferential Algebraic EquationsDynamic OptimizationSteam CrackingSupport Vector Machines

More Related Videos

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
11:53

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm

Published on: December 9, 2012

13.3K
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

3.4K

Related Experiment Videos

Last Updated: Dec 9, 2025

Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator
06:45

Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator

Published on: October 28, 2022

2.0K
Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
11:53

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm

Published on: December 9, 2012

13.3K
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

3.4K

Area of Science:

  • Computational Mathematics
  • Chemical Engineering
  • Machine Learning

Background:

  • Differential Algebraic Equations (DAEs) often present numerical integration challenges.
  • Traditional methods require full model discretization, which can be computationally intensive or infeasible.
  • Data-driven approaches offer an alternative for optimizing complex systems.

Purpose of the Study:

  • To develop a Support Vector Machine (SVM) based optimization framework for numerically infeasible DAEs.
  • To demonstrate the capability of SVMs in mapping numerical infeasibility boundaries.
  • To apply this data-driven methodology to complex reaction engineering problems.

Main Methods:

  • Formulating the numerical integration stability constraint of DAEs as a supervised classification problem.
  • Training, validating, and testing SVM models using data from DAE numerical integration.
  • Integrating SVM models with a constrained global grey-box optimization algorithm (ARGONAUT framework).

Main Results:

  • SVMs accurately map the boundary of numerical infeasibility for DAE systems.
  • The framework is successfully demonstrated on a 2D motivating example.
  • The methodology is extended and validated on a multi-dimensional case study in reaction engineering (thermal cracking of natural gas liquids).

Conclusions:

  • SVMs provide an effective data-driven approach for optimizing numerically challenging DAEs.
  • This method bypasses the need for full discretization of the first-principles model.
  • The integrated framework shows promise for complex optimization problems in chemical engineering.