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

245
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...
245
Methods of Obtaining Topography01:25

Methods of Obtaining Topography

235
Topography involves measuring and mapping land elevations, natural features, and artificial structures to create accurate representations of the terrain. Topographic surveying relies on traditional and modern methods, each with distinct advantages and limitations.Traditional Surveying Methods:Transit stadia surveys and plane table surveys were widely used traditional surveying methods. These techniques relied on instruments like theodolites and stadia rods for measuring distances and angles,...
235
Ampere-Maxwell's Law: Problem-Solving01:17

Ampere-Maxwell's Law: Problem-Solving

1.0K
A parallel-plate capacitor with capacitance C, whose plates have area A and separation distance d, is connected to a resistor R and a battery of voltage V. The current starts to flow at t = 0. What is the displacement current between the capacitor plates at time t? From the properties of the capacitor, what is the corresponding real current?
To solve the problem, we can use the equations from the analysis of an RC circuit and Maxwell's version of Ampère's law.
For the first part of the...
1.0K
Statically Indeterminate Problem Solving01:16

Statically Indeterminate Problem Solving

646
Statically indeterminate problems are those where statics alone can not determine the internal forces or reactions. Consider a structure comprising two cylindrical rods made of steel and brass. These rods are joined at point B and restrained by rigid supports at points A and C. Now, the reactions at points A and C and the deflection at point B are to be determined. This rod structure is classified as statically indeterminate as the structure has more supports than are necessary for maintaining...
646
Second Order systems II01:18

Second Order systems II

352
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.
352
Stability of Equilibrium Configuration: Problem Solving01:13

Stability of Equilibrium Configuration: Problem Solving

919
The stability of equilibrium configurations is an important concept in physics, engineering, and other related fields. In simple terms, it refers to the tendency of an object or system to return to its equilibrium position after being disturbed. The stability of an equilibrium configuration can be analyzed by considering the potential energy function of the system and examining its behavior near the equilibrium point.
Problem-solving in the context of the stability of equilibrium configuration...
919

You might also read

Related Articles

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

Sort by
Same author

Coexistence of Synchronization and Stochasticity in Thermally Coupled Mott Oscillators.

ACS nano·2026
Same author

Erratum: "Polynomial-time solution of prime factorization and NP-complete problems with digital memcomputing machines" [Chaos 27, 023107 (2017)].

Chaos (Woodbury, N.Y.)·2026
Same author

Memory in neural activity: Long-range order without criticality.

Physical review. E·2026
Same author

Chirality-Induced Spin-Orbit Coupling and Spin Selectivity.

The journal of physical chemistry. A·2025
Same author

Mixed-mode in-memory computing: towards high-performance logic processing in a memristive crossbar array.

Communications engineering·2025
Same author

Memory-induced long-range order in dynamical systems.

Physical review. E·2025

Related Experiment Video

Updated: Jan 1, 2026

Temporal Ordering of Dynamic Expression Data from Detailed Spatial Expression Maps
11:52

Temporal Ordering of Dynamic Expression Data from Detailed Spatial Expression Maps

Published on: February 9, 2017

6.4K

Taming a nonconvex landscape with dynamical long-range order: Memcomputing Ising benchmarks.

Forrest Sheldon1, Fabio L Traversa2, Massimiliano Di Ventra1

  • 1Department of Physics, University of California San Diego, La Jolla, California 92093, USA.

Physical Review. E
|December 25, 2019
PubMed
Summary
This summary is machine-generated.

Digital memcomputing machines solve complex optimization problems efficiently by leveraging collective dynamics, similar to quantum annealing. These machines use memory components to navigate complex landscapes, demonstrating polynomial-time solvability for challenging benchmarks.

More Related Videos

Photorealistic Learned Landscapes for Augmented Reality
06:54

Photorealistic Learned Landscapes for Augmented Reality

Published on: June 27, 2025

611
Generating Controlled, Dynamic Chemical Landscapes to Study Microbial Behavior
10:07

Generating Controlled, Dynamic Chemical Landscapes to Study Microbial Behavior

Published on: January 31, 2020

6.5K

Related Experiment Videos

Last Updated: Jan 1, 2026

Temporal Ordering of Dynamic Expression Data from Detailed Spatial Expression Maps
11:52

Temporal Ordering of Dynamic Expression Data from Detailed Spatial Expression Maps

Published on: February 9, 2017

6.4K
Photorealistic Learned Landscapes for Augmented Reality
06:54

Photorealistic Learned Landscapes for Augmented Reality

Published on: June 27, 2025

611
Generating Controlled, Dynamic Chemical Landscapes to Study Microbial Behavior
10:07

Generating Controlled, Dynamic Chemical Landscapes to Study Microbial Behavior

Published on: January 31, 2020

6.5K

Area of Science:

  • Computational Science
  • Quantum Computing
  • Optimization

Background:

  • Quantum annealing utilizes collective behavior for efficient problem-solving by enabling transitions in variable clusters.
  • Classical solvers with collective dynamics can also navigate complex, non-convex optimization landscapes effectively.
  • Understanding collective dynamics is key to developing advanced computational approaches for optimization.

Purpose of the Study:

  • To demonstrate that benchmarks from quantum annealing studies are solvable in polynomial time using digital memcomputing machines.
  • To illustrate the role of memory and collective dynamics in memcomputing machines.
  • To establish the advantages of computational methods based on collective dynamics of continuous systems.

Main Methods:

  • Utilized digital memcomputing machines, which employ dynamical components with memory to model optimization problems.
  • Proposed a simplified model of memcomputing machines to demonstrate emergent long-range order.
  • Applied the model to Ising frustrated-loop benchmarks to find the ground state.

Main Results:

  • Evidence that quantum annealing benchmarks are solvable in polynomial time using digital memcomputing machines.
  • Demonstrated emergent long-range order in a simplified memcomputing machine model.
  • Observed transient avalanches spanning the entire lattice in the Ising benchmark, linking long-range behavior to success probability.

Conclusions:

  • Digital memcomputing machines offer an efficient classical approach to solving complex optimization problems.
  • Collective dynamics and memory are crucial for the success of these computational solvers.
  • The study establishes the advantage of continuous dynamical systems with collective behavior for computational problem-solving.