معادلة الحرارة

Bayes' theorem describes the probability of an event based on prior knowledge of conditions that might be related to the event. It is a fundamental concept in probability theory and statistics. Mathematically, it is stated as [latex]P(A|B) = \frac{P(B|A)P(A)}{P(B)}[/latex], where A and B are events and [latex]P(B) \neq 0[/latex]. It relates the conditional and marginal probabilities of two random events.

A second-order linear elliptic partial differential equation that describes systems in a steady-state or equilibrium condition. It is written as [latex]nabla^2 u = 0[/latex] or [latex]Delta u = 0[/latex], where [latex]nabla^2[/latex] (or [latex]Delta[/latex]) is the Laplace operator. Solutions, called harmonic functions, are the smoothest possible functions and represent potentials in fields like electrostatics, gravitation, and fluid flow.

A standard approach for approximating solutions to overdetermined systems by finding model parameters that minimize the sum of the squared differences between observed and predicted values. This sum is known as the sum of squared residuals (SSR). The goal is to find the parameters [latex]\hat{\beta}[/latex] that minimize the function [latex]S(\beta) = \sum_{i=1}^{n} (y_i - x_i^T \beta)^2[/latex].

The coefficients for the Fourier series of a function [latex]s(x)[/latex] with period [latex]P[/latex] are calculated using integral formulas. The DC component is [latex]a_0 = \frac{2}{P} \int_{P} s(x) , dx[/latex]. The cosine coefficients are [latex]a_n = \frac{2}{P} int_{P} s(x) \cos\left(\frac{2pi n x}{P}\right) , dx[/latex], and the sine coefficients are [latex]b_n = \frac{2}{P} \int_{P} s(x) \sin\left(\frac{2\pi n x}{P}\right) , dx[/latex] for [latex]n ge 1[/latex].

A fundamental solution of a linear partial differential operator [latex]L[/latex] is a solution to the equation [latex]Lu = delta(x)[/latex], where [latex]delta(x)[/latex] is the Dirac delta function. It represents the response of the system to a point source or impulse. Once known, the solution to the inhomogeneous equation [latex]Lu = f(x)[/latex] can be found by convolution: [latex]u(x) = (G * f)(x)[/latex], where [latex]G[/latex] is the fundamental solution.

The Gauss-Bonnet theorem connects the geometry of a compact two-dimensional surface to its topology. It states that the integral of the Gaussian curvature [latex]K[/latex] over the entire surface [latex]M[/latex] is equal to [latex]2\pi[/latex] times the Euler characteristic [latex]\chi(M)[/latex] of the surface. The formula is [latex]\int_M K \, dA = 2\pi \chi(M)[/latex].

The Euler characteristic is a topological invariant, a number that describes a topological space's structure or shape regardless of how it is bent. For polyhedra, it is defined by the formula [latex]\chi = V - E + F[/latex], where V, E, and F are the number of vertices, edges, and faces, respectively. For a sphere, [latex]\chi = 2[/latex], while for a torus, [latex]\chi = 0[/latex].

One of the earliest problems in geometric probability, it is considered a precursor to the Monte Carlo method. It involves dropping a needle of length [latex]l[/latex] onto a floor with parallel lines a distance [latex]t[/latex] apart. The probability that the needle will cross a line is [latex]P = \frac{2l}{\pi t}[/latex] (for [latex]l \le t[/latex]). This provides a physical experiment to estimate [latex]\pi[/latex].

Parseval's theorem relates the total energy of a signal (the integral of its square over one period) to the sum of the squared energies of its Fourier series components. For a function [latex]s(x)[/latex] with period [latex]P[/latex], the theorem states: [latex]\frac{1}{P} \int_P |s(x)|^2 , dx = \sum_{n=-\infty}^{\infty} |c_n|^2[/latex], where [latex]c_n[/latex] are the complex Fourier coefficients.

Bayesian inference is a statistical method where Bayes' theorem is used to update the probability for a hypothesis as more evidence or information becomes available. It is a central tenet of Bayesian statistics. The core idea is expressed as: posterior probability is proportional to the product of the prior probability and the likelihood, [latex]p(\theta|D) \propto p(D|\theta)p(\theta)[/latex], where [latex]\theta[/latex] is the parameter and D is the data.

A Fourier series decomposes any periodic function or signal into a sum of simple oscillating functions, namely sines and cosines. For a function [latex]s(x)[/latex] with period [latex]P[/latex], the series is given by [latex]s(x) \approx \frac{a_0}{2} + \sum_{n=1}^{\infty} \left[ a_n \cos\left(\frac{2\pi n x}{P}\right) + b_n \sin\left(\frac{2\pi n x}{P}\right)\right][/latex]. The terms [latex]a_n[/latex] and [latex]b_n[/latex] are the Fourier coefficients.

Theorema Egregium (Latin for "Remarkable Theorem") states that the Gaussian curvature of a surface is an intrinsic property. This means it depends only on how distances are measured on the surface itself, not on how the surface is embedded in three-dimensional space. A flat sheet of paper can be rolled into a cylinder but not a sphere without stretching.

لكي تتقارب متسلسلة فورييه مع قيمة الدالة، يجب أن تُلبي الدالة شروط ديريشليت خلال فترة واحدة. وهذه الشروط هي: (1) أن تكون الدالة قابلة للتكامل المطلق، (2) أن يكون لها عدد محدود من القيم القصوى والدنيا، و(3) أن يكون لها عدد محدود من نقاط الانقطاع المحدودة.

De Morgan's laws are a pair of transformation rules in Boolean algebra that are fundamental to digital circuit design. The first law states that the negation of a conjunction is the disjunction of the negations: [latex]\neg(P \land Q) \iff (\neg P) \lor (\neg Q)[/latex]. The second states that the negation of a disjunction is the conjunction of the negations: [latex]\neg(P \lor Q) \iff (\neg P) \land (\neg Q)[/latex].