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

Protein Networks02:26

Protein Networks

4.1K
An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...
4.1K
Sequence Networks of Rotating Machines01:24

Sequence Networks of Rotating Machines

133
A Y-connected synchronous generator, grounded through a neutral impedance, is designed to produce balanced internal phase voltages with only positive-sequence components. The generator's sequence networks include a source voltage that is exclusively in the positive-sequence network. The sequence components of line-to-ground voltages at the generator terminals illustrate this configuration.
Zero-sequence current induces a voltage drop across the generator's neutral impedance and other...
133
Fast Decoupled and DC Powerflow01:24

Fast Decoupled and DC Powerflow

270
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:
270
Multimachine Stability01:25

Multimachine Stability

216
Multimachine stability analysis is crucial for understanding the dynamics and stability of power systems with multiple synchronous machines. The objective is to solve the swing equations for a network of M machines connected to an N-bus power system.
In analyzing the system, the nodal equations represent the relationship between bus voltages, machine voltages, and machine currents. The nodal equation is given by:
216
Predicting Reaction Outcomes02:24

Predicting Reaction Outcomes

8.5K
Kinetics describes the rate and path by which a reaction occurs. In contrast, thermodynamics deals with state functions and describes the properties, behavior, and components of a system. It is not concerned with the path taken by the process and cannot address the rate at which a reaction occurs. Although it does provide information about what can happen during a reaction process, it does not describe the detailed steps of what appears on an atomic or a molecular level. On the other hand,...
8.5K
Network Function of a Circuit01:25

Network Function of a Circuit

352
Frequency response analysis in electrical circuits provides vital insights into a circuit's behavior as the frequency of the input signal changes. The transfer function, a mathematical tool, is instrumental in understanding this behavior. It defines the relationship between phasor output and input and comes in four types: voltage gain, current gain, transfer impedance, and transfer admittance. The critical components of the transfer function are the poles and zeros.
352

You might also read

Related Articles

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

Sort by
Same author

Federated Learning over MU-MIMO Vehicular Networks.

Entropy (Basel, Switzerland)·2025
Same author

Reporting delays: A widely neglected impact factor in COVID-19 forecasts.

PNAS nexus·2024
Same author

Time-dependent solution of the NIMFA equations around the epidemic threshold.

Journal of mathematical biology·2020
Same author

Network-inference-based prediction of the COVID-19 epidemic outbreak in the Chinese province Hubei.

Applied network science·2020
Same journal

In This Issue.

Proceedings of the National Academy of Sciences of the United States of America·2026
Same journal

Long-term cultural continuity across the Neanderthal-modern human sequence at Üçağızlı II Cave, northern Levant.

Proceedings of the National Academy of Sciences of the United States of America·2026
Same journal

Dolphins use names to remember whom to avoid.

Proceedings of the National Academy of Sciences of the United States of America·2026
Same journal

Retraction for Shaked and Frenkel, Curiouser and curiouser: Meningeal lymphoid structures in the aging brain.

Proceedings of the National Academy of Sciences of the United States of America·2026
Same journal

Small but mighty: The outsized role of small water bodies in the global carbon cycle.

Proceedings of the National Academy of Sciences of the United States of America·2026
Same journal

Functional traits produce conditional outcomes in different community contexts.

Proceedings of the National Academy of Sciences of the United States of America·2026
See all related articles

Related Experiment Video

Updated: Aug 24, 2025

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
10:44

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

Published on: December 7, 2021

2.2K

Predicting network dynamics without requiring the knowledge of the interaction graph.

Bastian Prasse1, Piet Van Mieghem1

  • 1Faculty of Electrical Engineering, Mathematics and Computer Science, Delft University of Technology, 2600 GA Delft, The Netherlands.

Proceedings of the National Academy of Sciences of the United States of America
|October 24, 2022
PubMed
Summary
This summary is machine-generated.

Predicting network dynamics, like disease spread, is possible even without knowing the network structure. A new method uses past observations to build a surrogate network for accurate forecasting, revealing dynamics evolve in a low-dimensional subspace.

Keywords:
dynamics on networksnetwork reconstructionpredicting dynamics

More Related Videos

Real-time Electrophysiology: Using Closed-loop Protocols to Probe Neuronal Dynamics and Beyond
08:08

Real-time Electrophysiology: Using Closed-loop Protocols to Probe Neuronal Dynamics and Beyond

Published on: June 24, 2015

11.6K
Interfacing 3D Engineered Neuronal Cultures to Micro-Electrode Arrays: An Innovative In Vitro Experimental Model
09:47

Interfacing 3D Engineered Neuronal Cultures to Micro-Electrode Arrays: An Innovative In Vitro Experimental Model

Published on: October 18, 2015

10.1K

Related Experiment Videos

Last Updated: Aug 24, 2025

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
10:44

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

Published on: December 7, 2021

2.2K
Real-time Electrophysiology: Using Closed-loop Protocols to Probe Neuronal Dynamics and Beyond
08:08

Real-time Electrophysiology: Using Closed-loop Protocols to Probe Neuronal Dynamics and Beyond

Published on: June 24, 2015

11.6K
Interfacing 3D Engineered Neuronal Cultures to Micro-Electrode Arrays: An Innovative In Vitro Experimental Model
09:47

Interfacing 3D Engineered Neuronal Cultures to Micro-Electrode Arrays: An Innovative In Vitro Experimental Model

Published on: October 18, 2015

10.1K

Area of Science:

  • Complex Systems
  • Network Science
  • Nonlinear Dynamics

Background:

  • Network dynamics, such as disease propagation, are challenging to predict due to complex and often unknown network topologies.
  • Accurate forecasting requires understanding both the network structure (graph) and the governing equations of the dynamics.

Purpose of the Study:

  • To demonstrate that network dynamics can be predicted without prior knowledge of the network topology.
  • To develop a generalizable algorithm for predicting dynamics on complex networks using only observational data.

Main Methods:

  • A two-step prediction algorithm was proposed: 1. Constructing a surrogate network by fitting nodal state observations to the dynamical model. 2. Iterating the governing equations on this surrogate network.

Main Results:

  • The proposed method accurately predicts network dynamics over a significant time horizon, even with unknown true topology.
  • Predictions remain accurate across various observation times and noise levels.
  • The effectiveness stems from the dynamics evolving within a low-dimensional subspace, independent of graph size and heterogeneity.

Conclusions:

  • Network topology is not essential for predicting dynamics, challenging conventional approaches in nonlinear dynamics on complex networks.
  • This work offers a novel perspective on understanding and forecasting behavior in interconnected systems.
  • The findings have implications for fields like epidemiology, social network analysis, and infrastructure monitoring.