Tags: Monte Carlo

Description

Monte Carlo methods are a class of computational algorithms that rely on repeated random sampling to compute their results. Monte Carlo methods are often used in simulating physical and mathematical systems. Because of their reliance on repeated computation of random or pseudo-random numbers, these methods are most suited to calculation by a computer and tend to be used when it is unfeasible or impossible to compute an exact result with a deterministic algorithm.

Learn more about quantum dots from the many resources on this site, listed below. More information on Monte Carlo method can be found here.

Online Presentations (1-20 of 24)

  1. Anisotropic Schrödinger Equation Quantum Corrections for 3D Monte Carlo Simulations of Nanoscale Multigate Transistors

    05 Jan 2016 | | Contributor(s):: Karol Kalna, Muhammad Ali A. Elmessary, Daniel Nagy, Manuel Aldegunde

    IWCE 2015 presentation. We incorporated anisotropic 2D Schrodinger equation based quantum corrections (SEQC) that depends on valley orientation into a 3D Finite Element (FE) Monte Carlo (MC) simulation toolbox. The MC toolbox was tested against experimental ID-VG characteristics of the 22 nm...

  2. Atomistic Modeling: Past, Present, and Future, MGI, ICME, etc.

    03 Nov 2015 | | Contributor(s):: Paul Saxe

    I will present a perspective on atomistic modeling — tools using quantum methods such as DFT, as well as molecular dynamics and Monte Carlo methods based on forcefields — over the past 30 years or so. While we are all caught up in the present, it is important to remember and realize...

  3. Discussion about Ion Channels Using Reduced Model Approaches

    21 Sep 2011 | | Contributor(s):: James Fonseca

    The seminar will cover the reasons how the channels are able to selectively permit the flow of certain species of ions while blocking other physiological cations.

  4. ECE 656 Lecture 30: Balance Equation Approach III

    01 Dec 2009 | | Contributor(s):: Mark Lundstrom

    OutlineCarrier Temperature and Heat FluxBalance equations in 3DHeterostructuresSummary

  5. ECE 656 Lecture 31: Monte Carlo Simulation

    01 Dec 2009 | | Contributor(s):: Mark Lundstrom

    Outline:IntroductionReview of carrier scatteringSimulating carrier trajectoriesFree flightCollisionUpdate after collisionPutting it all togetherSummary

  6. ECE 656 Lecture 32: Balance Equation Approach III

    19 Jan 2012 | | Contributor(s):: Mark Lundstrom

    Outline:Review of L31Carrier temperature and heat fluxHeterostructuresSummary

  7. ECE 656 Lecture 34a: Monte Carlo Simulation I

    21 Feb 2012 | | Contributor(s):: Mark Lundstrom

    OutlineIntroductionReview of carrier scatteringSimulating carrier trajectoriesFree flightCollisionUpdate after collisionPutting it all togetherSummary

  8. ECE 656 Lecture 34b: Monte Carlo Simulation II

    21 Feb 2012 | | Contributor(s):: Mark Lundstrom

    OutlineIntroductionReview of carrier scatteringSimulating carrier trajectoriesFree flightCollisionUpdate after collisionPutting it all togetherSummary

  9. ECE 656 Lecture 41: Transport in a Nutshell

    21 Feb 2012 | | Contributor(s):: Mark Lundstrom

  10. ECE 695A Lecture 14a: Voltage Dependent HCI I

    19 Feb 2013 | | Contributor(s):: Muhammad Alam

    Outline:Background and Empirical ObservationsTheory of Hot Carriers: Hydrodynamic ModelTheory of Hot Carriers: Monte Carlo ModelTheory of Hot Carriers: Universal ScalingConclusionAppendices

  11. ECE 695A Lecture 14b: Voltage Dependent HCI II

    19 Feb 2013 | | Contributor(s):: Muhammad Alam

    Outline:Background and Empirical ObservationsTheory of Hot Carriers: Hydrodynamic ModelTheory of Hot Carriers: Monte Carlo ModelTheory of Hot Carriers: Universal ScalingConclusionAppendices

  12. High Dimensional Uncertainty Quantification via Multilevel Monte Carlo

    04 Feb 2016 | | Contributor(s):: Hillary Fairbanks

    Multilevel Monte Carlo (MLMC) has been shown to be a cost effective way to compute moments of desired quantities of interest in stochastic partial differential equations when the uncertainty in the data is high-dimensional. In this talk, we investigate the improved performance of MLMC versus...

  13. Illinois PHYS 466, Lecture 18: Kinetic Monte Carlo (KMC)

    04 May 2009 | | Contributor(s):: David M. Ceperley, Omar N Sobh

  14. Lecture 3: The Wigner Monte Carlo Method for Density Functional Theory

    18 Nov 2014 | | Contributor(s):: Jean Michel D Sellier

    In this lecture, Dr. Sellier discusses the Wigner Monte Carlo method in the framework of density functional theory (DFT).

  15. Materials Science on the Atomic Scale with the 3-D Atom Probe

    08 Nov 2006 |

    Some of the key goals of materials science and technology are to be able to design a material from first principles, to predict its behaviour, and also to optimise the processing route for its manufacture. In recent years, these goals have come closer to realisation, thanks in part to the...

  16. Molecular modeling of lipid bilayer edge and hybrid-MCMD method: Implementation and application

    29 Apr 2008 | | Contributor(s):: Yong Jiang

    Introduction to mixed lipid systems, Hybrid Monte Carlo and MD (atomistic) algorithm for mixed lipid systems

  17. Multilevel Markov Chain Monte Carlo for Uncertainty Quantification in Subsurface Flow

    02 Feb 2016 | | Contributor(s):: Christian Ketelsen

    The multilevel Monte Carlo method has been shown to be an effective variance reduction technique for quantifying uncertainty in subsurface flow simulations when the random conductivity field can be represented by a simple prior distribution. In state-of-the-art subsurface simulation the...

  18. Particle Simulations of Ion Generation and Transport in Microelectromechanical Systems and Micropropulsion

    29 May 2012 | | Contributor(s):: Venkattraman Ayyaswamy

    The first part of the talk deals with use of the PIC method with Monte Carlo collisions (MCC) between electrons and the ambient neutral gas to develop models to predict charge accumulation, breakdown voltage, etc. for various ambient gases, gap sizes, cathode material, and frequency of applied...

  19. Practical Introduction to the BioMOCA Suite

    23 Apr 2008 | | Contributor(s):: David Papke

    In this presentation, I describe how to use the online BioMOCA Suite. I explain how to prepare the .pqr input protein structure from a .pdb structure. I then explain in detail how to use each of the four subtools in the BioMOCA Suite.I do not cover in detail how the BioMOCA code works. If you...

  20. Quantum and Thermal Effects in Nanoscale Devices

    18 Sep 2008 | | Contributor(s):: Dragica Vasileska

    To investigate lattice heating within a Monte Carlo device simulation framework, we simultaneously solve the Boltzmann transport equation for the electrons, the 2D Poisson equation to get the self-consistent fields and the hydrodynamic equations for acoustic and optical phonons. The phonon...