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Distributions and weak derivatives
Test functions, the Dirac delta, differentiating anything, weak derivatives and weak solutions.
Many functions that matter have corners or jumps (a shock, a plucked string, \( |x| \)), and some "functions" that matter are not functions at all (a point source). Distributions handle all of them by testing. A test function \( \varphi \) is infinitely differentiable and vanishes outside a bounded set. Every locally integrable \( f \) defines the functional \( \varphi \mapsto \int f\varphi\,dx \), and a distribution is any continuous linear functional on test functions. The Dirac delta \( \delta(\varphi) = \varphi(0) \) is one that comes from no function.
The definition of the derivative is forced by integration by parts. For smooth \( f \), \( \int f'\varphi = -\int f\varphi' \) because \( \varphi \) vanishes at the ends. So for any distribution \( T \) we define \( T'(\varphi) = -T(\varphi') \). Every distribution now has derivatives of every order. The Heaviside step \( H \) has \( H'(\varphi) = -\int_0^\infty\varphi' = \varphi(0) \), so \( H' = \delta \).
A function \( g \in L^1_{\text{loc}} \) is the weak derivative of \( f \) when \( \int f\varphi'\,dx = -\int g\varphi\,dx \) for every test function. The weak derivative of \( |x| \) is \( \operatorname{sign}(x) \), as the example finds: split the integral at 0 and integrate by parts on each side, and the boundary terms at 0 cancel because \( |x| \) is continuous. The weak derivative of \( \operatorname{sign}(x) \) is not a function; as a distribution it is \( 2\delta \), and the jump is exactly what produced it.
Picture it: you are not allowed to look at \( f \) directly, only at its blurred averages \( \int f\varphi \) through every possible smooth lens \( \varphi \). Sharp corners and spikes are invisible to each single lens, but the collection of all of them determines the object, and moving the derivative onto the lens costs nothing.
Think it: a weak solution of \( -\Delta u = f \) is a \( u \) with \( \int\nabla u\cdot\nabla\varphi = \int f\varphi \) for all test \( \varphi \). Existence of weak solutions follows from Hilbert space arguments (the Riesz representation theorem), and a separate regularity theory then asks whether they are smooth. That two-step strategy is exactly the shape of the Navier-Stokes problem: Leray proved in 1934 that weak solutions exist for all time, for all finite-energy divergence-free data. Whether smooth data always give smooth solutions is the open question; whether Leray's solutions are unique is also unsettled without a force (with a suitably chosen force, Albritton, Brué and Colombo showed in 2022 that uniqueness fails).
Gumagana halimbawa · derivative of abs(x)
Hakbang-hakbang
- \frac{d}{dx}\left[\left|{x}\right|\right]
Start from the derivative to compute.
- \operatorname{sign}{\left(x \right)} \frac{d}{d x} x
d/dx |u| = sign(u)·u′.
- \operatorname{sign}{\left(x \right)}
d/dx of x is 1.
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Paano: Distributions and weak derivatives
- Write the defining identity: ∫ f φ' dx = -∫ g φ dx for all test functions φ.
- Split the integral at every point where f fails to be smooth.
- Integrate by parts on each piece and collect the boundary terms at the break points.
- If the boundary terms cancel (f continuous), g is the piecewise derivative; if f jumps, a multiple of δ appears.
Mga katanungan na tanong ng mga tao
Can you multiply two distributions?
Not in general: δ² has no sensible meaning. This is a real obstruction for nonlinear PDEs and is why weak solutions of nonlinear equations need enough integrability that products like u times u make sense as functions.
Is a weak derivative unique?
Up to changes on a set of measure zero, yes: if two functions give the same integrals against every test function, they agree almost everywhere.
What should I know before starting?
Partial derivatives, the divergence theorem and multiple integrals from multivariable calculus; linear second-order ODEs; eigenvalues from linear algebra; and enough analysis to be comfortable with uniform convergence and integrals over infinite intervals.
Why are there so few formulas for solutions?
Explicit solutions exist for linear equations with constant coefficients on simple domains. Almost everything else (curved domains, variable coefficients, nonlinear terms) has none, so the modern subject proves that a solution exists and estimates its size and smoothness without ever writing it down.
How does this course lead to the Navier-Stokes problem?
The Navier-Stokes equations are a heat equation for the velocity with a transport term and a pressure that is found from a Poisson equation. Reading the Millennium problem needs energy estimates, weak solutions, Sobolev spaces, local existence with a blow-up criterion, and the scaling argument that explains why three dimensions is hard. The last six lessons build those.
What do elliptic, parabolic and hyperbolic mean?
Elliptic equations (Laplace) describe equilibrium and smooth everything; parabolic equations (heat) describe diffusion forward in time; hyperbolic equations (waves) carry signals at finite speed and keep their sharp edges. The type decides which data make a sensible problem.
Higit pa sa Partial Differential Equations
What a PDE is, and how to classify oneFirst-order linear equations and characteristicsNonlinear first-order equations and shocksThe heat equation: where it comes fromSeparation of variablesFourier series: coefficients, convergence and ParsevalThe heat equation on an intervalThe wave equation and d'Alembert's formulaThe vibrating string: modes and energyLaplace's equation and harmonic functionsMaximum principles and uniquenessGreen's functions and the fundamental solutionThe Fourier transform and the heat kernelEnergy methods: uniqueness, stability and decay