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 38)

  1. SCALE Electronics, Photonics, and Space, Oh My! - An Introduction to the EPICA Program

    Online Presentations | 02 Jan 2024 | Contributor(s):: Hannah Dattilo

  2. Teaching and Learning with the MIT Atomic Scale Modeling Toolkit's Classical and Quantum Atomic Modeling Applications

    Online Presentations | 23 Dec 2022 | Contributor(s):: Enrique Guerrero

     We will perform molecular dynamics computations using LAMMPS, simple Monte Carlo simulations including the Ising model, and run quantum chemistry and density functional theory computations.

  3. Machine Learning Predicts Additive Manufacturing Part Quality: Tutorial on Support Vector Regression

    Online Presentations | 26 Aug 2022 | Contributor(s):: Davis McGregor

    This tutorial introduces and demonstrates the use of machine learning (ML) to address this need. Using data collected from an AM factory, you will train a support vector regression (SVR) model to predict the dimensions of AM parts based on the design geometry and manufacturing parameters.

  4. IWCN 2021: Effective Monte Carlo Simulator of Hole Transport in SiGe alloys

    Online Presentations | 25 Jul 2021 | Contributor(s):: Caroline dos Santos Soares, Alan Rossetto, Dragica Vasileska, Gilson Wirth

    In this work, an Ensemble Monte Carlo (EMC) transport simulator is presented for simulation of hole transport in SiGe alloys.

  5. IWCN 2021: Computational Research of CMOS Channel Material Benchmarking for Future Technology Nodes: Missions, Learnings, and Remaining Challenges

    Online Presentations | 15 Jul 2021 | Contributor(s):: raseong kim, Uygar Avci, Ian Alexander Young

    In this preentation, we review our journey of doing CMOS channel material benchmarking for future technology nodes. Through the comprehensive computational research for past several years, we have successfully projected the performance of various novel material CMOS based on rigorous physics...

  6. ME 597UQ Lecture 24: Bayesian Model Comparison using Sequential Monte Carlo

    Online Presentations | 27 Apr 2018 | Contributor(s):: Ilias Bilionis

  7. Memory-Efficient Particle Annihilation Algorithm for Wigner Monte Carlo Simulations

    Online Presentations | 10 Feb 2016 | Contributor(s):: Paul Ellinghaus

    IWCE 2015 presentation. The Wigner Monte Carlo solver, using the signed-particle method, is based on the generation and annihilation of numerical particles. The memory demands of the annihilation algorithm can become exorbitant, if a high spatial resolution is used, because the entire discretized...

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

    Online Presentations | 04 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...

  9. High Dimensional Uncertainty Quantification via Multilevel Monte Carlo

    Online Presentations | 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...

  10. Study of the Interface Roughness Models using 3D Finite Element Schrödinger Equation Corrected Monte Carlo Simulator on Nanoscaled FinFET

    Online Presentations | 25 Jan 2016 | Contributor(s):: Daniel Nagy, Muhammad Ali A. Elmessary, Manuel Aldegunde, Karol Kalna

    IWCE 2015 presentation.  Interface roughness scattering (IRS) is one of the key limiting scattering mechanism for both planar and non-planar CMOS devices. To predict the performance of future scaled devices and new structures the quantum mechanical confinement based IRS models are essential....

  11. Sensitivity Analysis of Multiscale Reaction Networks with Stochastic Averaging

    Online Presentations | 25 Jan 2016 | Contributor(s):: Araz Ryan Hashemi

    We shall show how stochastic averaging may be employed to speed computations and obtain estimates of mean values and sensitivities with respect to the steady state distribution. Further, we shall establish bounds which show the bias induced by the averaging method decays to zero as the disparity...

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

    Online Presentations | 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 gate...

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

    Online Presentations | 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...

  14. Lecture 1: The Wigner Formulation of Quantum Mechanics

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

    In this lecture, Dr. Sellier discusses the Wigner formulation of Quantum Mechanics which is based on the concept of quasi-distributions defined over the phase-space.

  15. Lecture 2: The Wigner Monte Carlo Method for Single-Body Quantum Systems

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

    In this lecture, Dr. Sellier discusses the Wigner Monte Carlo method applied to single-body quantum systems.

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

    Online Presentations | 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).

  17. Lecture 4: The ab-initio Wigner Monte Carlo Method

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

    In this lecture, Dr. Sellier discusses the ab-initio Wigner Monte Carlo method for the simulation of strongly correlated systems.

  18. Lecture 5: Systems of Identical Fermions in the Wigner Formulation of Quantum Mechanics

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

    In this lecture, Dr. Sellier discusses about systems of indistinguishable Fermions in the Wigner formulation of quantum mechanics.

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

    Online Presentations | 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

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

    Online Presentations | 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