Mediaspace scheduled maintenance: Aug 25, 2026 07:00 - 12:00 AM. During this time, videos will be temporarily unavailable. Check status updates.

Lecture

Circular Motion: Dynamics and Speed

In course

PHYS-100: Advanced physics I (mechanics)

La Physique Générale I (avancée) couvre la mécanique du point et du solide indéformable. Apprendre la mécanique, c'est apprendre à mettre sous forme mathématique un phénomène physique, en modélisant la situation et appliquant les lois de la physique. ...

Description

This lecture covers advanced physics topics related to circular motion, dynamics of material points, and speed calculations. It discusses concepts such as decomposition of vectors, three-dimensional movement, and curvilinear abscissa. The presentation also delves into the relationship between speed, displacement, and acceleration in circular motion.

This video is available exclusively on Mediaspace for a restricted audience. Please log in to MediaSpace to access it if you have the necessary permissions.

Watch on Mediaspace

Instructor

Paolo Ricci

Paolo Ricci earned his masters degree in nuclear engineering at the Politecnico di Torino, Turin (Italy) in 2000. His doctoral studies were conducted at the Los Alamos National Laboratory, with focus on kinetic simulation of magnetic reconnection in the Earth's magnetotail. He spent two-and-a-half years as a postdoctoral researcher at Dartmouth College's Department of Physics and Astronomy, where he worked on gyrokinetic simulations of the Z pinch. He joined the EPFL's Swiss Plasma Center (SPC), as a EURATOM fellow in 2006, was named Tenure Track Assistant Professor in June 2010, and Associate Professor in August 2016. He is at the head of the SPC theory group. Paolo Ricci is the recipient of the 2016 Section de Physique Teaching Prize and of the 2021 Craie d'Or award from the EPFL physics bachelor students.

Official source

Related concepts (32)

QR decomposition

In linear algebra, a QR decomposition, also known as a QR factorization or QU factorization, is a decomposition of a matrix A into a product A = QR of an orthonormal matrix Q and an upper triangular matrix R. QR decomposition is often used to solve the linear least squares problem and is the basis for a particular eigenvalue algorithm, the QR algorithm. Any real square matrix A may be decomposed as where Q is an orthogonal matrix (its columns are orthogonal unit vectors meaning ) and R is an upper triangular matrix (also called right triangular matrix).

Cholesky decomposition

In linear algebra, the Cholesky decomposition or Cholesky factorization (pronounced ʃəˈlɛski ) is a decomposition of a Hermitian, positive-definite matrix into the product of a lower triangular matrix and its conjugate transpose, which is useful for efficient numerical solutions, e.g., Monte Carlo simulations. It was discovered by André-Louis Cholesky for real matrices, and posthumously published in 1924. When it is applicable, the Cholesky decomposition is roughly twice as efficient as the LU decomposition for solving systems of linear equations.

Singular value decomposition

In linear algebra, the singular value decomposition (SVD) is a factorization of a real or complex matrix. It generalizes the eigendecomposition of a square normal matrix with an orthonormal eigenbasis to any matrix. It is related to the polar decomposition. Specifically, the singular value decomposition of an complex matrix M is a factorization of the form where U is an complex unitary matrix, is an rectangular diagonal matrix with non-negative real numbers on the diagonal, V is an complex unitary matrix, and is the conjugate transpose of V.

LU decomposition

In numerical analysis and linear algebra, lower–upper (LU) decomposition or factorization factors a matrix as the product of a lower triangular matrix and an upper triangular matrix (see matrix decomposition). The product sometimes includes a permutation matrix as well. LU decomposition can be viewed as the matrix form of Gaussian elimination. Computers usually solve square systems of linear equations using LU decomposition, and it is also a key step when inverting a matrix or computing the determinant of a matrix.

Schur decomposition

In the mathematical discipline of linear algebra, the Schur decomposition or Schur triangulation, named after Issai Schur, is a matrix decomposition. It allows one to write an arbitrary complex square matrix as unitarily equivalent to an upper triangular matrix whose diagonal elements are the eigenvalues of the original matrix. The Schur decomposition reads as follows: if A is an n × n square matrix with complex entries, then A can be expressed as where Q is a unitary matrix (so that its inverse Q−1 is also the conjugate transpose Q* of Q), and U is an upper triangular matrix, which is called a Schur form of A.

Related lectures (40)

Orthogonal Projection: Vector Decomposition

Explains orthogonal projection and vector decomposition with examples in particle trajectory analysis.

Circular Motion and Harmonic Oscillator

Explores circular motion, harmonic oscillators, and double springs, elucidating the physics principles behind them.

QR Factorization: Least Squares System Resolution MOOC: Linear Algebra (Part 3)

Covers the QR factorization method applied to solving a system of linear equations in the least squares sense.

Orthogonal Projections and Reflections

Covers the analytical expression of orthogonal projections and reflections in 2D space.

Trajectories and Motion Equations