Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

State Space Representation

State Space Representation

The frequency-domain technique, commonly used in analyzing and designing feedback control systems, is effective for linear, time-invariant systems. However, it falls short when dealing with nonlinear, time-varying, and multiple-input multiple-output systems. The time-domain or state-space approach addresses these limitations by utilizing state variables to construct simultaneous, first-order differential equations, known as state equations, for an nth-order system.
Consider an RLC circuit, a...

Correlations

Correlations

Correlation means that there is a relationship between two or more variables (such as ice cream consumption and crime), but this relationship does not necessarily imply cause and effect. When two variables are correlated, it simply means that as one variable changes, so does the other. We can measure correlation by calculating a statistic known as a correlation coefficient. A correlation coefficient is a number from -1 to +1 that indicates the strength and direction of the relationship between...

Correlation and Regression

Correlation and Regression

In statistics, correlation describes the degree of association between two variables. In the subfield of linear regression, correlation is mathematically expressed by the correlation coefficient, which describes the strength and direction of the relationship between two variables. The coefficient is symbolically represented by 'r' and ranges from -1 to +1. A positive value indicates a positive correlation where the two variables move in the same direction. A negative value suggests a negative...

Correlation

Correlation

In statistics, two variables are said to be correlated if the values of one variable are associated with the other variable. Depending on the relationship between two variables, correlation can be of three types– positive correlation, negative correlation, and zero correlation.
Two variables, for example, a and b, are said to be positively correlated if both variables move in the same direction. In other words, a positive correlation exists between two variables, a and b, if:

Multi-input and Multi-variable systems

Multi-input and Multi-variable systems

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 of...

Sequence Networks of Rotating Machines

Sequence Networks of Rotating Machines

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...

You might also read

Related Articles

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

Sort by

Same author

Moiré-Induced Optical Nonlinearities: Single- and Multiphoton Resonances.

Physical review letters·2022

Same author

Publisher's Note: "Essential chaotic dynamics of chatter in turning processes" [Chaos 30, 053108 (2020)].

Chaos (Woodbury, N.Y.)·2020

Same author

Essential chaotic dynamics of chatter in turning processes.

Chaos (Woodbury, N.Y.)·2020

Same author

Topological bands for ultracold atoms.

Reviews of modern physics·2020

Same author

P-Band Induced Self-Organization and Dynamics with Repulsively Driven Ultracold Atoms in an Optical Cavity.

Physical review letters·2019

Same author

Chromaticity separation and the alpha response.

Neuropsychologia·2017

Search research articles

Related Experiment Videos

Local tensor network for strongly correlated projective States.

B Béri¹, N R Cooper

¹Theory of Condensed Matter Group, Cavendish Laboratory, J. J. Thomson Avenue, Cambridge CB3 0HE, United Kingdom.

Physical Review Letters

|May 17, 2011

Summary

Researchers developed a local Grassmann tensor network to simulate strongly correlated quantum systems. This new method enables encoding calculations for projective states, overcoming previous limitations in tensor network simulations.

Related Experiment Videos

Area of Science:

Quantum physics
Computational physics
Condensed matter theory

Background:

Tensor network methods are vital for simulating complex quantum systems.
Encoding strongly correlated projective states, like fractional quantum Hall states, in local tensor networks remains a significant challenge.
Previous approaches have struggled to represent these states effectively.

Purpose of the Study:

To introduce a novel tensor network construction for simulating strongly correlated quantum systems.
To demonstrate the capability of encoding calculations for challenging quantum states.
To provide a framework for utilizing physically motivated trial wave functions in variational calculations.

Main Methods:

Development of a local Grassmann tensor network.
Explicit construction for encoding calculations of local operator averages.
Integration with physically motivated trial wave functions for variational tensor network calculations.

Main Results:

Successfully encoded calculations for averages of local operators within a local tensor network.
Demonstrated a method to represent strongly correlated projective states, previously unachievable with local tensor networks.
Established a practical approach for tensor network variational calculations using established wave functions.

Conclusions:

The developed Grassmann tensor network offers a viable solution for simulating strongly correlated quantum systems with projective states.
This work overcomes a key limitation in tensor network simulations, broadening their applicability.
Physically motivated trial wave functions can now be effectively incorporated into tensor network variational methods.