500 particles in an elliptical billiard. The segments are velocity vectors, the two dots are the focal points of the ellipse. C code: https://www.idpoisson.fr/berglund/vid_particles_ellipse.c Music: Cold Blue by Astron@Sound Therapy - Best Sleep and Relaxation Music For more informati
From playlist Particles in billiards
Animation of Fermilab's Accelerator Complex
The 6,800-acre Fermilab site is home to a chain of particle accelerators that provide particle beams to numerous experiments and R&D programs. This 2-minute animation explains how the proton source provides the particles that get accelerated and travel through the accelerator complex at cl
From playlist Detectors and Accelerators
4.2: Many Particle Systems (Emitters!) - The Nature of Code
In this video, I add an Emitter class to further organize the array of particles and allow for multiple particle systems in a p5.js sketch. https://thecodingtrain.com/learning/nature-of-code/4.2-particle-emmiters.html p5.js Web Editor Sketches: 🕹️ Particle Emitters: https://editor.p5js.or
From playlist The Nature of Code 2
Physics - Ch 66 Ch 4 Quantum Mechanics: Schrodinger Eqn(63 of 92) Transmission vs Reflection-Classic
Visit http://ilectureonline.com for more math and science lectures! In this video I will explain what happens in the “real” (non-quantum mechanic) world when a particle comes upon a barrier or a step-function where the energy of the particle is related to the potential of that step. With
From playlist PHYSICS 66.1 QUANTUM MECHANICS - SCHRODINGER EQUATION
Daniel Schwen - MOOSE: Parallel sampling for rare event probabilities & inverse Bayesian inference
Recorded 30 March 2023. Daniel Schwen of the Idaho National Laboratory presents "Parallel sampling for computing rare event probabilities, inverse Bayesian inference and, uncertainty quantification with MOOSE" at IPAM's Increasing the Length, Time, and Accuracy of Materials Modeling Using
From playlist 2023 Increasing the Length, Time, and Accuracy of Materials Modeling Using Exascale Computing
4.1: Particle Systems - The Nature of Code
This video introduces the idea of Particle Systems. Read along: http://natureofcode.com/book/chapter-4-particle-systems/ Reeves: http://dl.acm.org/citation.cfm?id=357320 Sims: http://www.karlsims.com/particle-dreams.html, http://www.karlsims.com/papers/ParticlesSiggraph90.pdf Help us ca
From playlist 4: Particle Systems - The Nature of Code
Learn how to determine when a particle is at rest using a calculator
Keywords 👉 Learn how to solve particle motion problems. Particle motion problems are usually modeled using functions. Now, when the function modeling the position of the particle is given with respect to the time, we find the speed function of the particle by differentiating the function
From playlist Particle Motion Problems
DDPS | Neural Galerkin schemes with active learning for high-dimensional evolution equations
Title: Neural Galerkin schemes with active learning for high-dimensional evolution equations Speaker: Benjamin Peherstorfer (New York University) Description: Fitting parameters of machine learning models such as deep networks typically requires accurately estimating the population loss
From playlist Data-driven Physical Simulations (DDPS) Seminar Series
Anais Crestetto: Micro-macro discretizations
The lecture was held within the of the Hausdorff Junior Trimester Program: Kinetic Theory Abstract: This talk aims to present asymptotic preserving (AP) schemes, based on micro-macro decomposition and particle method, for kinetic equations of Boltzmann-BGK type in the diffusive scaling.
From playlist HIM Lectures: Junior Trimester Program "Kinetic Theory"
Physics - Ch 66.5 Quantum Mechanics: The Hydrogen Atom (27 of 78) Magnetic Quantum Number
Visit http://ilectureonline.com for more math and science lectures! In this video I will further explain the orbital magnetic quantum number m(l). For each subshell s, p, d, f, g,...defined by the corresponding orbital quantum numbers l=0, l=1, l=2, l= 3,... There are a number of angular
From playlist PHYSICS 66.5 QUANTUM MECHANICS: THE HYDROGEN ATOM
An introduction to Lattice Quantum Chromodynamics by Nilmani Mathur
Nonperturbative and Numerical Approaches to Quantum Gravity, String Theory and Holography DATE: 27 January 2018 to 03 February 2018 VENUE: Ramanujan Lecture Hall, ICTS Bangalore The program "Nonperturbative and Numerical Approaches to Quantum Gravity, String Theory and Holography" aims
From playlist Nonperturbative and Numerical Approaches to Quantum Gravity, String Theory and Holography
Julia Charrier: Subsurface flow with uncertainty : applications and numerical analysis issues
Abstract: In this talk we first quickly present a classical and simple model used to describe flow in porous media (based on Darcy's Law). The high heterogeneity of the media and the lack of data are taken into account by the use of random permability fields. We then present some mathemati
From playlist Numerical Analysis and Scientific Computing
Quantum Transport, Lecture 11: Quantum Bits
Instructor: Sergey Frolov, University of Pittsburgh, Spring 2013 http://sergeyfrolov.wordpress.com/ Summary: DiVincenzo criteria for hardware design of quantum bits, Rabi, Ramsey, spin echo experiments, candidate quantum bit systems. Quantum Transport course development supported in part b
From playlist Quantum Transport
Anthony Maggs: Irreversible Monte Carlo methods for particle simulations
HYBRID EVENT Recorded during the meeting "On Future Synergies for Stochastic and Learning Algorithms" the September 28, 2021 by the Centre International de Rencontres Mathématiques (Marseille, France) Filmmaker: Guillaume Hennenfent Find this video and other talks given by worldwide m
From playlist Probability and Statistics
Antoine Rousseau: Wind energy: from downscaled forecasts to mills production
Find this video and other talks given by worldwide mathematicians on CIRM's Audiovisual Mathematics Library: http://library.cirm-math.fr. And discover all its functionalities: - Chapter markers and keywords to watch the parts of your choice in the video - Videos enriched with abstracts, b
From playlist Mathematical Physics
Classical Monte Carlo of Frustrated Systems (Tutorial) by Ludovic Jaubert
PROGRAM FRUSTRATED METALS AND INSULATORS (HYBRID) ORGANIZERS: Federico Becca (University of Trieste, Italy), Subhro Bhattacharjee (ICTS-TIFR, India), Yasir Iqbal (IIT Madras, India), Bella Lake (Helmholtz-Zentrum Berlin für Materialien und Energie, Germany), Yogesh Singh (IISER Mohali, In
From playlist FRUSTRATED METALS AND INSULATORS (HYBRID, 2022)
What are the W and Z particles?
Subscribe to our YouTube Channel for all the latest from World Science U. Visit our Website: http://www.worldscienceu.com/ Like us on Facebook: https://www.facebook.com/worldscienceu Follow us on Twitter: https://twitter.com/worldscienceu
From playlist Science Unplugged: Particle Physics
David Ceperley - Introduction to Classical and Quantum Monte Carlo methods for Many-Body systems
Recorded 09 March 2022. David Ceperley of the University of Illinois at Urbana-Champaign presents "Introduction to Classical and Quantum Monte Carlo methods for Many-Body systems" at IPAM's Advancing Quantum Mechanics with Mathematics and Statistics Tutorials. Abstract: Metropolis (Markov
From playlist Tutorials: Advancing Quantum Mechanics with Mathematics and Statistics - March 8-11, 2022
The Map of Particle Physics | The Standard Model Explained
In this video I explain all the basics of particle physics and the standard model of particle physics. Check out Brilliant here: https://brilliant.org/DOS/ Buy the poster here: https://store.dftba.com/collections/domain-of-science/products/map-of-fundamental-particles Digital version here:
From playlist Quantum Physics Videos - Domain of Science
Sebastian Reich (DDMCS@Turing): Learning models by making them interact
Complex models in all areas of science and engineering, and in the social sciences, must be reduced to a relatively small number of variables for practical computation and accurate prediction. In general, it is difficult to identify and parameterize the crucial features that must be incorp
From playlist Data driven modelling of complex systems