R Programming Introduction: Matrices (R intro-05)
[script is here https://github.com/bionicturtle/youtube/tree/master/r-intro] In R a matrix is an atomic vector with the dimension attribute. In this example, the correlation matrix is entered as a vector with sixteen elements: rho_v ＜-c(1.000, ...). Then the vector is translated into a mat
From playlist R Programming: Intro
Understanding Matrices and Matrix Notation
In order to do linear algebra, we will have to know how to use matrices. So what's a matrix? It's just an array of numbers listed in a grid of particular dimensions that can represent the coefficients and constants from a system of linear equations. They're fun, I promise! Let's just start
From playlist Mathematics (All Of It)
In this lesson we learn about matrices: two-dimensional data structures in R with rows and columns. Matrices are a building block to learning about more complicated tabular data structures like data frames which are used extensively in data science. This is lesson 6 of a 30-part introduct
From playlist Introduction to R
R Programming: Introduction: List data structure (R Intro-02)
[My R notebook file script is here https://github.com/bionicturtle/youtube/tree/master/r-intro] Unlike atomic vectors, list (vectors) are flexible: each element can be a different type (char, integer, numeric, logical or even a sub-list!). List[i] returns the i-th element as a list, while
From playlist R Programming: Intro
1.6 Arrays and matrices in R | statistical analysis and data science course Rstudio | Dimensional
In this chapter of the video series in the crash course in statistics and data science with R / Rstudio we will see the definition, utilization, and importance of arrays with R. Also, we discuss their extension from vectors to matrices. Part 1: Definition - What is an array? - Array or
From playlist R Tutorial | Rstudio
Gábor Csányi: "Representation and regression problems in molecular structure and dynamics"
Machine Learning for Physics and the Physics of Learning 2019 Workshop IV: Using Physical Insights for Machine Learning "Representation and regression problems in molecular structure and dynamics" Gábor Csányi - University of Cambridge Abstract: A vast proportion of total global comput
From playlist Machine Learning for Physics and the Physics of Learning 2019
Find the Transformation Matrix Given Two Transformations in R3 Using T(e1), T(e2), and T(e3)
This video explains how to use the transformation of the standard basis vectors to find a transformation matrix in R3 given two vector transformations.
From playlist Matrix (Linear) Transformations
Find a Transformation Matrix (Standard Matrix) Given a Matrix Transformation: R3 to R3
This video explains 2 ways to determine a transformation matrix given the equations for a matrix transformation.
From playlist Matrix (Linear) Transformations
David Bowler - Large-scale and linear scaling DFT: why we need it, and how we do it - IPAM at UCLA
Recorded 29 March 2023. David Bowler of University College London presents "Large-scale and linear scaling DFT: why we need it, and how we do it" at IPAM's Increasing the Length, Time, and Accuracy of Materials Modeling Using Exascale Computing workshop. Abstract: We will survey the underl
From playlist 2023 Increasing the Length, Time, and Accuracy of Materials Modeling Using Exascale Computing
Peter Benner: "Parallel Algorithms for Tensor Train Arithmetic"
Tensor Methods and Emerging Applications to the Physical and Data Sciences 2021 Workshop I: Tensor Methods and their Applications in the Physical and Data Sciences "Parallel Algorithms for Tensor Train Arithmetic" Peter Benner - Max Planck Institute for Dynamics of Complex Technical Syste
From playlist Tensor Methods and Emerging Applications to the Physical and Data Sciences 2021
Matrix Models, Gauge-Gravity Duality, and Simulations on the Lattice (Lecture 2) by Georg Bergner
NONPERTURBATIVE AND NUMERICAL APPROACHES TO QUANTUM GRAVITY, STRING THEORY AND HOLOGRAPHY (HYBRID) ORGANIZERS: David Berenstein (University of California, Santa Barbara, USA), Simon Catterall (Syracuse University, USA), Masanori Hanada (University of Surrey, UK), Anosh Joseph (IISER Mohal
From playlist NUMSTRING 2022
Unifying machine learning and quantum chemistry with a deep neural network | AISC
For slides and more information on the paper, visit https://ai.science/e/unifying-machine-learning-and-quantum-chemistry-with-a-deep-neural-network--LzeGRqxc5Sg38jE9eJjE Speaker: Reinhard J. Maurer; Discussion Moderator: Mehrshad Esfahani
From playlist ML in Chemistry
1.5 Matrices and operations in R | statistical analysis and data science course Rstudio | Algebra
In this chapter of the video series in the crash course in statistics and data science with R / Rstudio we will see the definition, utilization, and importance of matrices with R. Also, we discuss the different algebraic operations like sum, subtraction and multiplication, as well as inver
From playlist R Tutorial | Rstudio
Unitary and non-unitary photonics simulations of molecular quantum dynamics by Yogesh Joglekar
Non-Hermitian Physics - PHHQP XVIII DATE: 04 June 2018 to 13 June 2018 VENUE:Ramanujan Lecture Hall, ICTS Bangalore Non-Hermitian Physics-"Pseudo-Hermitian Hamiltonians in Quantum Physics (PHHQP) XVIII" is the 18th meeting in the series that is being held over the years in Quantum Phys
From playlist Non-Hermitian Physics - PHHQP XVIII
Tensor Network Methods in Four Dimensional Field Theory by Daisuke Kadoh
PROGRAM Nonperturbative and Numerical Approaches to Quantum Gravity, String Theory and Holography (ONLINE) ORGANIZERS: David Berenstein (UCSB), Simon Catterall (Syracuse University), Masanori Hanada (University of Surrey), Anosh Joseph (IISER, Mohali), Jun Nishimura (KEK Japan), David Sc
From playlist Nonperturbative and Numerical Approaches to Quantum Gravity, String Theory and Holography (Online)
Lecture 04-Jack Simons Electronic Structure Theory- Linear combinations of atomic orbitals
The Hartree-Fock molecular orbitals; LCAO-MO expansion; Hartree-Fock equations in matrix form; one- and two-electron integrals; the iterative SCF process; scaling with basis set size; how virtual orbitals change with basis set; core, valence, polarization, and diffuse basis functions; Slat
From playlist U of Utah: Jack Simons' Electronic Structure Theory course
Linear Algebra for Computer Scientists. 12. Introducing the Matrix
This computer science video is one of a series of lessons about linear algebra for computer scientists. This video introduces the concept of a matrix. A matrix is a rectangular or square, two dimensional array of numbers, symbols, or expressions. A matrix is also classed a second order
From playlist Linear Algebra for Computer Scientists
Classical Monte Carlo of Frustrated Systems (Tutorial - 2) 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)
Find an Image of a Vector Under a 3 by 2 Matrix Transformation
This video explains how to find an image of an input vector under a linear transformation with a 3 by 2 standard matrix.
From playlist Matrix (Linear) Transformations
Periodically driven closed quantum systems: periodic steady state By Amit Dutta
Indian Statistical Physics Community Meeting 2016 URL: https://www.icts.res.in/discussion_meeting/details/31/ DATES Friday 12 Feb, 2016 - Sunday 14 Feb, 2016 VENUE Ramanujan Lecture Hall, ICTS Bangalore This is an annual discussion meeting of the Indian statistical physics community wh
From playlist Indian Statistical Physics Community Meeting 2016