Lecture

Scalar, Vector or Tensor? Gravity

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In courses (2)

PHYS-427: Relativity and cosmology I

Introduce the students to general relativity and its classical tests. ...

PHYS-428: Relativity and cosmology II

This course is the basic introduction to modern cosmology. It introduces students to the main concepts and formalism of cosmology, the observational status of Hot Big Bang theory and discusses major physical processes in the early Universe. ...

Description

This lecture explores the main points of defining antisymmetric tensor in arbitrary coordinate systems, discussing the dimensionality of space-time, formulating the challenge for constructing a relativistic theory of gravity, and deriving equations of motion for free particles. The instructor presents the challenges faced in formulating a relativistic theory of gravity, the limitations of scalar gravity, and the importance of considering gravity as a tensor field. The lecture concludes with a discussion on the equivalence principle and the implications of gravitational mass in the context of electromagnetic fields.

Instructor

Victor Gorbenko

Teaching & PhD PhD Students Jonas Dujava, Anders Wallberg, Mattia Varrone, Aleksandr Zhabin, Veronica Sacchi Courses Classical electrodynamics PHYS-324 The goal of this course is the study of the physical and conceptual consequences of Maxwell equations. Relativity and cosmology II PHYS-428 This course is the basic introduction to modern cosmology. It introduces students to the main concepts and formalism of cosmology, the observational status of Hot Big Bang theory and dis

Official source

Ontological neighbourhood

Physics

Relativity: General relativity, Special relativity

Mechanics: Classical mechanics

Related concepts (32)

Electromagnetic tensor

In electromagnetism, the electromagnetic tensor or electromagnetic field tensor (sometimes called the field strength tensor, Faraday tensor or Maxwell bivector) is a mathematical object that describes the electromagnetic field in spacetime. The field tensor was first used after the four-dimensional tensor formulation of special relativity was introduced by Hermann Minkowski. The tensor allows related physical laws to be written very concisely, and allows for the quantization of the electromagnetic field by Lagrangian formulation described below.

Dimension

In physics and mathematics, the dimension of a mathematical space (or object) is informally defined as the minimum number of coordinates needed to specify any point within it. Thus, a line has a dimension of one (1D) because only one coordinate is needed to specify a point on it - for example, the point at 5 on a number line. A surface, such as the boundary of a cylinder or sphere, has a dimension of two (2D) because two coordinates are needed to specify a point on it - for example, both a latitude and longitude are required to locate a point on the surface of a sphere.

Four-dimensional space

Four-dimensional space (4D) is the mathematical extension of the concept of three-dimensional space (3D). Three-dimensional space is the simplest possible abstraction of the observation that one needs only three numbers, called dimensions, to describe the sizes or locations of objects in the everyday world. For example, the volume of a rectangular box is found by measuring and multiplying its length, width, and height (often labeled x, y, and z).

Electromagnetic field

An electromagnetic field (also EM field or EMF) is a classical (i.e. non-quantum) field produced by moving electric charges. It is the field described by classical electrodynamics (a classical field theory) and is the classical counterpart to the quantized electromagnetic field tensor in quantum electrodynamics (a quantum field theory). The electromagnetic field propagates at the speed of light (in fact, this field can be identified as light) and interacts with charges and currents.

Dimensionality reduction

Dimensionality reduction, or dimension reduction, is the transformation of data from a high-dimensional space into a low-dimensional space so that the low-dimensional representation retains some meaningful properties of the original data, ideally close to its intrinsic dimension. Working in high-dimensional spaces can be undesirable for many reasons; raw data are often sparse as a consequence of the curse of dimensionality, and analyzing the data is usually computationally intractable (hard to control or deal with).

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