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Linear Approximation in Time Domain01:21

Linear Approximation in Time Domain

Nonlinear systems often require sophisticated approaches for accurate modeling and analysis, with state-space representation being particularly effective. This method is especially useful for systems where variables and parameters vary with time or operating conditions, such as in a simple pendulum or a translational mechanical system with nonlinear springs.
For a simple pendulum with a mass evenly distributed along its length and the center of mass located at half the pendulum's length, the...
Linear time-invariant Systems01:23

Linear time-invariant Systems

A system is linear if it displays the characteristics of homogeneity and additivity, together termed the superposition property. This principle is fundamental in all linear systems. Linear time-invariant (LTI) systems include systems with linear elements and constant parameters.
The input-output behavior of an LTI system can be fully defined by its response to an impulsive excitation at its input. Once this impulse response is known, the system's reaction to any other input can be calculated...
BIBO stability of continuous and discrete -time systems01:24

BIBO stability of continuous and discrete -time systems

System stability is a fundamental concept in signal processing, often assessed using convolution. For a system to be considered bounded-input bounded-output (BIBO) stable, any bounded input signal must produce a bounded output signal. A bounded input signal is one where the modulus does not exceed a certain constant at any point in time.
To determine the BIBO stability, the convolution integral is utilized when a bounded continuous-time input is applied to a Linear Time-Invariant (LTI) system.
Transmission-Line Differential Equations01:26

Transmission-Line Differential Equations

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 the...
Entropy Change in Reversible Processes01:10

Entropy Change in Reversible Processes

In the Carnot engine, which achieves the maximum efficiency between two reservoirs of fixed temperatures, the total change in entropy is zero. The observation can be generalized by considering any reversible cyclic process consisting of many Carnot cycles. Thus, it can be stated that the total entropy change of any ideal reversible cycle is zero.
The statement can be further generalized to prove that entropy is a state function. Take a cyclic process between any two points on a p-V diagram.
Second Order systems II01:18

Second Order systems II

In an underdamped second-order system, where the damping ratio ζ is between 0 and 1, a unit-step input results in a transfer function that, when transformed using the inverse Laplace method, reveals the output response. The output exhibits a damped sinusoidal oscillation, and the difference between the input and output is termed the error signal. This error signal also demonstrates damped oscillatory behavior. Eventually, as the system reaches a steady state, the error diminishes to zero.
If  ζ...

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Related Experiment Video

Updated: May 30, 2026

Age-dependent Dynamics of Locomotion in Caenorhabditis elegans: A Lyapunov Exponent Analysis
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Age-dependent Dynamics of Locomotion in Caenorhabditis elegans: A Lyapunov Exponent Analysis

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A unitary perturbation theory approach to real-time evolution problems.

A Hackl1, S Kehrein

  • 1Institut für Theoretische Physik, Universität zu Köln, Zülpicher Straße 77, 50937 Köln, Germany.

Journal of Physics. Condensed Matter : an Institute of Physics Journal
|August 6, 2011
PubMed
Summary

We introduce a new analytical method for real-time quantum many-body system evolution. This approach accurately solves Heisenberg equations of motion for dissipative quantum systems, providing insights into non-equilibrium dynamics.

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Area of Science:

  • Quantum physics
  • Many-body systems
  • Quantum dynamics

Background:

  • Real-time evolution of quantum systems is computationally challenging.
  • Dissipative quantum systems require accurate methods for analyzing dynamics.
  • Continuous unitary transformations offer a framework for quantum evolution.

Purpose of the Study:

  • To present a novel analytical approach for real-time evolution in quantum many-body systems.
  • To extend the continuous unitary transformations framework.
  • To demonstrate the accuracy of the proposed method for dissipative systems.

Main Methods:

  • Developing a novel solution method for Heisenberg equations of motion.
  • Applying the method to study dissipative quantum systems across all timescales.
  • Obtaining results for non-equilibrium correlation functions.

Main Results:

  • The analytical approach accurately describes real-time evolution in dissipative quantum systems.
  • Non-equilibrium correlation functions are derived for general initial conditions.
  • The method is illustrated using the exactly solvable dissipative oscillator and the dissipative two-state system.

Conclusions:

  • The proposed analytical approach offers a powerful tool for studying quantum many-body dynamics.
  • This method provides accurate solutions for dissipative quantum systems.
  • The framework is applicable to various quantum systems and initial conditions.