StudentsEducators

Kaluza-Klein Theory

The Kaluza-Klein theory is a groundbreaking approach in theoretical physics that attempts to unify general relativity and electromagnetism by introducing additional spatial dimensions. Originally proposed by Theodor Kaluza in 1921 and later extended by Oskar Klein, the theory posits that our universe consists of not just the familiar four dimensions (three spatial dimensions and one time dimension) but also an extra compact dimension that is not directly observable. This extra dimension is theorized to be curled up or compactified, making it imperceptible at everyday scales.

In mathematical terms, the theory modifies the Einstein field equations to accommodate this additional dimension, leading to a geometric interpretation of electromagnetic phenomena. The resulting equations suggest that the electromagnetic field can be derived from the geometry of the higher-dimensional space, effectively merging gravity and electromagnetism into a single framework. The Kaluza-Klein theory laid the groundwork for later developments in string theory and higher-dimensional theories, demonstrating the potential of extra dimensions in explaining fundamental forces in nature.

Other related terms

contact us

Let's get started

Start your personalized study experience with acemate today. Sign up for free and find summaries and mock exams for your university.

logoTurn your courses into an interactive learning experience.
Antong Yin

Antong Yin

Co-Founder & CEO

Jan Tiegges

Jan Tiegges

Co-Founder & CTO

Paul Herman

Paul Herman

Co-Founder & CPO

© 2025 acemate UG (haftungsbeschränkt)  |   Terms and Conditions  |   Privacy Policy  |   Imprint  |   Careers   |  
iconlogo
Log in

Farkas Lemma

Farkas Lemma is a fundamental result in linear inequalities and convex analysis, providing a criterion for the solvability of systems of linear inequalities. It states that for a given matrix AAA and vector bbb, at least one of the following statements is true:

  1. There exists a vector xxx such that Ax≤bAx \leq bAx≤b.
  2. There exists a vector yyy such that ATy=0A^T y = 0ATy=0 and y≥0y \geq 0y≥0 while also ensuring that bTy<0b^T y < 0bTy<0.

This lemma essentially establishes a duality relationship between feasible solutions of linear inequalities and the existence of certain non-negative linear combinations of the constraints. It is widely used in optimization, particularly in the context of linear programming, as it helps in determining whether a system of inequalities is consistent or not. Overall, Farkas Lemma serves as a powerful tool in both theoretical and applied mathematics, especially in economics and resource allocation problems.

Partition Function Asymptotics

Partition function asymptotics is a branch of mathematics and statistical mechanics that studies the behavior of partition functions as the size of the system tends to infinity. In combinatorial contexts, the partition function p(n)p(n)p(n) counts the number of ways to express the integer nnn as a sum of positive integers, regardless of the order of summands. As nnn grows large, the asymptotic behavior of p(n)p(n)p(n) can be captured using techniques from analytic number theory, leading to results such as Hardy and Ramanujan's formula:

p(n)∼14n3eπ2n3p(n) \sim \frac{1}{4n\sqrt{3}} e^{\pi \sqrt{\frac{2n}{3}}}p(n)∼4n3​1​eπ32n​​

This expression reveals that p(n)p(n)p(n) grows rapidly, exhibiting exponential growth characterized by the term eπ2n3e^{\pi \sqrt{\frac{2n}{3}}}eπ32n​​. Understanding partition function asymptotics is crucial for various applications, including statistical mechanics, where it relates to the thermodynamic properties of systems and the study of phase transitions. It also plays a significant role in number theory and combinatorial optimization, linking combinatorial structures with algebraic and geometric properties.

Capital Asset Pricing Model

The Capital Asset Pricing Model (CAPM) is a financial theory that establishes a linear relationship between the expected return of an asset and its systematic risk, represented by the beta coefficient. The model is based on the premise that investors require higher returns for taking on additional risk. The expected return of an asset can be calculated using the formula:

E(Ri)=Rf+βi(E(Rm)−Rf)E(R_i) = R_f + \beta_i (E(R_m) - R_f)E(Ri​)=Rf​+βi​(E(Rm​)−Rf​)

where:

  • E(Ri)E(R_i)E(Ri​) is the expected return of the asset,
  • RfR_fRf​ is the risk-free rate,
  • βi\beta_iβi​ is the measure of the asset's risk in relation to the market,
  • E(Rm)E(R_m)E(Rm​) is the expected return of the market.

CAPM is widely used in finance for pricing risky securities and for assessing the performance of investments relative to their risk. By understanding the relationship between risk and return, investors can make informed decisions about asset allocation and investment strategies.

Pulse-Width Modulation Efficiency

Pulse-Width Modulation (PWM) is a technique used to control the power delivered to electrical devices by varying the width of the pulses in a signal. The efficiency of PWM refers to how effectively this method converts input power into usable output power without excessive losses. Key factors influencing PWM efficiency include the frequency of the PWM signal, the load being driven, and the characteristics of the switching components (like transistors) used in the circuit.

In general, PWM is considered efficient because it minimizes heat generation, as the switching devices are either fully on or fully off, leading to lower power losses compared to linear regulation. The efficiency can be quantified using the formula:

Efficiency(η)=PoutPin×100%\text{Efficiency} (\eta) = \frac{P_{\text{out}}}{P_{\text{in}}} \times 100\%Efficiency(η)=Pin​Pout​​×100%

where PoutP_{\text{out}}Pout​ is the output power delivered to the load, and PinP_{\text{in}}Pin​ is the input power from the source. Hence, high PWM efficiency is crucial in applications like motor control and power supply systems, where maintaining energy efficiency is essential for performance and thermal management.

Gluon Radiation

Gluon radiation refers to the process where gluons, the exchange particles of the strong force, are emitted during high-energy particle interactions, particularly in Quantum Chromodynamics (QCD). Gluons are responsible for binding quarks together to form protons, neutrons, and other hadrons. When quarks are accelerated, such as in high-energy collisions, they can emit gluons, which carry energy and momentum. This emission is crucial in understanding phenomena such as jet formation in particle collisions, where streams of hadrons are produced as a result of quark and gluon interactions.

The probability of gluon emission can be described using perturbative QCD, where the emission rate is influenced by factors like the energy of the colliding particles and the color charge of the interacting quarks. The mathematical treatment of gluon radiation is often expressed through equations involving the coupling constant gsg_sgs​ and can be represented as:

dNdE∝αs⋅1E2\frac{dN}{dE} \propto \alpha_s \cdot \frac{1}{E^2}dEdN​∝αs​⋅E21​

where NNN is the number of emitted gluons, EEE is the energy, and αs\alpha_sαs​ is the strong coupling constant. Understanding gluon radiation is essential for predicting outcomes in high-energy physics experiments, such as those conducted at the Large Hadron Collider.

Phillips Curve

The Phillips Curve represents an economic concept that illustrates the inverse relationship between the rate of inflation and the rate of unemployment within an economy. Originally formulated by A.W. Phillips in 1958, the curve suggests that when unemployment is low, inflation tends to rise, and conversely, when unemployment is high, inflation tends to decrease. This relationship can be expressed mathematically as:

π=πe−β(U−Un)\pi = \pi^e - \beta (U - U^n)π=πe−β(U−Un)

where:

  • π\piπ is the inflation rate,
  • πe\pi^eπe is the expected inflation rate,
  • UUU is the actual unemployment rate,
  • UnU^nUn is the natural rate of unemployment,
  • and β\betaβ is a positive constant.

However, the validity of the Phillips Curve has been debated, especially during periods of stagflation, where high inflation and high unemployment occurred simultaneously. Over time, economists have adjusted the model to include factors such as expectations and supply shocks, leading to the development of the New Keynesian Phillips Curve, which incorporates expectations about future inflation.