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Partial Differential Equations and Characteristics
1 · Prerequisites
- Absolute and Conditional Convergence; Rearrangement; Products
- Algebraic Closure, Embeddings, and Separability
- Algebraic Extensions, Extension Degree, and Finite Fields
- Binary Operations, Monoids, Groups and Subgroups
- Compactness in Metric Spaces
- Completeness, Completion, and Uniform Continuity
- Composition Series, the Jordan–Hölder Theorem and Solvable Groups
- Congruences, the Integers Modulo n and the Chinese Remainder Theorem
- Connectedness
- Construction of the Natural Numbers
- Construction of the Real Numbers via Cauchy Sequences
- Construction of the Real Numbers via Dedekind Cuts
- Continuity, IVT, EVT, and Uniform Continuity
- Cosets, Index and Lagrange's Theorem
- Countability and Uncountability
- Cyclic Groups and Direct Products
- Determinants of Matrices over a Commutative Ring
- Diagonalisation and the Minimal Polynomial
- Divisibility, Euclidean Domains, Principal Ideal Domains and Unique Factorisation
- Divisibility, Greatest Common Divisors and Bézout's Identity
- Dual Spaces, Bilinear and Quadratic Forms, and Sylvester's Law of Inertia
- Eigenvalues, Eigenvectors and the Characteristic Polynomial
- Filters and Ultrafilters
- Finite Counting, Factorials and Binomial Coefficients
- Foundations of the Real Numbers for Analysis
- Group Actions, Orbits, Stabilisers and Cayley's Theorem
- Group Homomorphisms and the Isomorphism Theorems
- Ideals, Quotient Rings and the Isomorphism Theorems for Rings
- Inner Product Spaces, Gram-Schmidt, Projections and Adjoints
- Limits of Real Functions
- limsup, liminf, and Subsequential Limits
- Linear Independence, Bases and Dimension
- Linear Transformations, Rank-Nullity and Quotient Spaces
- Matrices, the Matrix of a Linear Map, and Change of Basis
- Metric Spaces
- Mixed Partials, Taylor Formulae, and Extrema
- Monotone Functions, Discontinuities, and Continuity Sets
- Monotone Sequences, Bolzano-Weierstrass, and Cauchy Completeness
- Normal Subgroups and Quotient Groups
- Order, Zorn's Lemma, and the Axiom of Choice
- Picard-Lindelöf and First-Order Ordinary Differential Equations
- Polynomial Rings, the Division Algorithm and Roots
- Power Series and Real-Analytic Functions
- Primes, Euclid's Lemma and the Fundamental Theorem of Arithmetic
- Properties of the Integral and the Working FTC
- Relations, Functions, and Quotients
- Rings, Subrings, Integral Domains and Fields
- Rⁿ as a Normed Space; Vector-Valued Functions
- Roots, Rational Powers, and Classical Inequalities
- Sequences and Limits
- Sequences and Series of Functions; Uniform Convergence
- Series: Convergence and the Nonnegative Tests
- Simple Field Extensions and the Construction of the Complex Numbers
- Splitting Fields
- Subspaces, Products, and Quotients
- Suprema and Infima
- Sylow's Theorems, p-Groups and Nilpotent Groups
- Symmetric Groups, Cycle Decomposition and the Sign Homomorphism
- The Derivative and the Mean Value Theorems
- The Determinant of a Linear Operator, Cofactors and Cramer's Rule
- The Exponential Function
- The Fundamental Theorem of Algebra
- The Fundamental Theorem of Finite Abelian Groups
- The Galois Correspondence
- The Inverse and Implicit Function Theorems
- The Riemann Integral: Definition and Integrability
- The Spectral Theorem, Positive Operators and Singular Value Decomposition
- The Total Derivative in ℝᵐ → ℝⁿ
- The ZFC Axioms and the Basic Set Constructions
- Topological Spaces and Continuity
- Topology of ℝ
- Triangularisation, Generalised Eigenspaces and Jordan Canonical Form
- Vector Spaces, Linear Subspaces, Span and Direct Sums
2 · Summary
This page introduces PDE language in the scalar classical setting, isolates the principal symbol as the coordinate-stable part of the highest-order operator, and uses it to define characteristic covectors and the second-order elliptic/parabolic/hyperbolic trichotomy. In two variables, the discriminant and characteristic directions recover the familiar pointwise type test, while constant coefficients admit the standard linear canonical forms and the page records exactly where that reduction stops.
The second half develops linear transport by characteristics. The chain rule reduces the PDE to a scalar ODE along each characteristic, the inverse flow gives the homogeneous and inhomogeneous formulas, and a local dependence-on-initial-position lemma supplies the transversality argument for the noncharacteristic Cauchy problem.
3 · Logical flowchart
4 · Definitions, theorems and proofs
Scalar partial differential equations, order, and classical solutions
Definition
Let be open, let , and let
be a function of the point and of the jet coordinates , where is the number of multi-indices of length at most . Assume that depends nontrivially on at least one jet coordinate. A scalar partial differential equation of order at most is an equation
for an unknown scalar field .
Its order is the largest for which depends nontrivially on . A classical solution of an equation of order at most is a function satisfying the displayed equation at every .
If is a codimension-one surface, prescribing values of and possibly of some derivatives on is Cauchy data. If is used instead, the prescribed values are boundary data.
Linear, semilinear, quasilinear, and fully nonlinear partial differential equations
Definition
Consider an order- scalar PDE on .
It is linear when it has the form
so every jet variable enters affinely and with coefficients depending only on .
It is semilinear when the top-order derivatives enter linearly with coefficients depending only on , while lower-order terms may depend nonlinearly on .
It is quasilinear when the top-order derivatives still enter linearly, but their coefficients may depend on .
It is fully nonlinear when the dependence on the order- jet is not affine. Thus semilinear and quasilinear equations are affine in the top-order variables after the lower jet is fixed, whereas fully nonlinear equations are not. The classification is by dependence on the highest-order jet, not by how the equation happens to be typeset.
Principal part and principal symbol of a scalar PDE
Definition
Let
be a linear scalar differential operator of order on an open .
Its principal part is the homogeneous order- operator
Its principal symbol is the homogeneous polynomial in the covector given by
If the equation is quasilinear, first freeze the lower jet at the point under discussion; the resulting linear operator in the highest derivatives has a principal part and principal symbol defined by the same formula.
The principal symbol depends only on the first derivative of a smooth coordinate change
Statement
Let
be a linear scalar differential operator of order , let be a smooth local coordinate change with smooth inverse, and let be the operator in the -coordinates defined by . Then
for every covector , and every derivative of of order at least contributes only to lower-order terms of .
Facts & Assumptions
Given: An order- operator , a smooth local diffeomorphism , and the pulled-back unknown .
The principal symbol is the homogeneous order- polynomial formed from the top-order coefficients of a linear scalar operator (Principal part and principal symbol of a scalar PDE).
The chain rule differentiates a composite by the derivative of the outer map applied to the derivative of the inner map (The chain rule for total derivatives: ).
Ordered mixed partial derivatives of the same order agree when the needed regularity is present (Continuous mixed partials of order are invariant under permutations).
Proof
For a first -derivative, [L2] gives , so each differentiation either lands on a derivative of or on one factor .
Repeating step 1.1 times produces a sum of terms, and the only terms containing an order- derivative of are those in which every differentiation lands on the -factor; once a differentiation lands on a coefficient , the remaining differentiations can raise the order of the -derivative by at most , so every derivative of of order at least contributes only to lower-order terms of .
In the top-order part, each application of step 1.1 contributes one factor of the pulled-back covector, and [L3] lets us reorder the differentiations without changing the result; therefore [L1] identifies the transformed principal symbol as , which depends only on the first derivative of the coordinate change.
Characteristic covectors, hypersurfaces, and noncharacteristic data
Definition
Let be the principal symbol of an order- scalar equation.
A nonzero covector is characteristic at when
If is a hypersurface locally written as with on , then is characteristic at when its conormal is characteristic, and noncharacteristic at otherwise.
Accordingly, Cauchy data prescribed on are noncharacteristic near when the defining conormal is nowhere characteristic there.
Characteristic hypersurfaces are independent of the defining function
Statement
Let be a hypersurface with and on . If is the principal symbol of an order- scalar operator, then
for every .
Facts & Assumptions
Given: Two defining functions for the same hypersurface , and the principal symbol .
Characteristic covectors and characteristic hypersurfaces are defined by vanishing of the principal symbol on the conormal (Characteristic covectors, hypersurfaces, and noncharacteristic data).
The principal symbol is homogeneous of degree in the covector variable (Principal part and principal symbol of a scalar PDE).
Proof
Fix . Any tangent vector is the velocity of a curve in , so because both defining functions vanish on ; thus and have the same kernel, namely the tangent hyperplane . Since both covectors are nonzero, the annihilator of that hyperplane is one-dimensional, so there is a unique scalar with .
By [L2], homogeneity gives for every ; since , these values vanish together, so [L1] shows that the characteristic property is independent of the chosen defining function.
Elliptic, hyperbolic, and parabolic principal symbols
Definition
For a real scalar second-order principal symbol
assume .
The symbol is elliptic at when for every ; equivalently, the quadratic form has one definite sign.
Fix a nonzero covector . The symbol is strictly hyperbolic at relative to when and, for every not proportional to , the polynomial has two distinct real roots. The first condition says that is noncharacteristic and prevents the root condition from becoming vacuous in one dimension.
A space-time operator of the form
is parabolic at when the spatial quadratic form is positive semidefinite and nonzero, and, in the standard time covector, the equation contains a first-order time derivative rather than a second-order one. The nonzero condition ensures that the operator still has a second-order spatial principal part at the point.
A symmetric second-order principal part has a coordinate-invariant signature normal form
Statement
Fix a point of a scalar second-order operator whose principal part is
Then there is a linear change of coordinates near in which the frozen principal quadratic form becomes
with the remaining directions absent. The triple depends only on the quadratic form, not on the chosen coordinates.
Facts & Assumptions
Given: The symmetric coefficient matrix of the frozen principal part at .
The order- principal symbol is the quadratic polynomial associated to the symmetric coefficient matrix (Elliptic, hyperbolic, and parabolic principal symbols).
A self-adjoint endomorphism of a finite-dimensional real inner product space has an orthonormal eigenbasis (Real spectral theorem: a self-adjoint endomorphism of a finite-dimensional real inner product space has an orthonormal eigenbasis).
A real symmetric bilinear form is congruent to exactly one diagonal form (Sylvester's law of inertia: every real symmetric form is congruent to , and is unique).
Proof
By [L1], the frozen principal symbol is , and because is symmetric, [L2] gives an orthonormal eigenbasis in which is diagonal with real eigenvalues .
Rescaling each coordinate with by changes the nonzero diagonal entries to or and leaves the zero eigenvalues unchanged, so the principal form becomes ; by [L3], the numbers of positive, negative, and zero directions are intrinsic.
Therefore the frozen symmetric principal part has the stated signature normal form, and its signature triple is invariant under further invertible linear coordinate changes.
The discriminant for a second-order equation in two variables
Definition
For a second-order equation in two variables with principal part
the discriminant is
At a point where , the principal part is called elliptic when , parabolic when , and hyperbolic when . If at the point, the second-order principal part vanishes there and is degenerate rather than parabolic.
Here “parabolic” names the nonzero rank-one case in the pointwise classification of a binary second-order principal form. This is distinct from the heat-type space-time convention, which requires a first-order time derivative and a nonzero semidefinite spatial principal form.
In two variables, type and characteristic directions are coordinate invariant
Statement
For a scalar second-order equation in two variables, the sign of the discriminant is unchanged by a smooth local coordinate change. Moreover, the characteristic directions are exactly the tangent directions whose normal covectors are characteristic, so they are also coordinate invariant.
Facts & Assumptions
Given: A second-order principal part and a smooth local coordinate change with Jacobian matrix .
The discriminant is for a two-variable second-order principal part (The discriminant for a second-order equation in two variables).
Under a smooth coordinate change, the transformed principal symbol is the old symbol evaluated on the pulled-back covector (The principal symbol depends only on the first derivative of a smooth coordinate change).
Characteristic hypersurfaces are defined by vanishing of the principal symbol on their conormal, and that notion is independent of the defining function (Characteristic covectors, hypersurfaces, and noncharacteristic data, Characteristic hypersurfaces are independent of the defining function).
Proof
Write the quadratic symbol matrix as . By [L2], if has Jacobian , then the transformed principal symbol is , so the new matrix is . Therefore , and because [L1] gives , the transformed discriminant is ; its sign is unchanged.
If a curve is written locally as , then its tangent vector is annihilated by the normal covector , so in two dimensions is a quarter-turn of up to a nonzero scalar; the curve is characteristic exactly when by [L3], equivalently when , and because [L3] makes characteristic conormals coordinate invariant, the corresponding tangent directions are coordinate invariant as well.
Constant-coefficient second-order equations in two variables have canonical principal forms
Statement
Let
have constant real coefficients in its principal part.
If , an invertible linear change of variables and multiplication by a nonzero scalar reduce the principal part to .
If , such a change reduces it to .
If but , such a change reduces it to .
Facts & Assumptions
Given: The constant symmetric matrix of the principal quadratic form.
A symmetric second-order principal part has a signature normal form whose signature is coordinate invariant (A symmetric second-order principal part has a coordinate-invariant signature normal form).
In two variables the sign of is coordinate invariant (In two variables, type and characteristic directions are coordinate invariant).
The discriminant is (The discriminant for a second-order equation in two variables).
Proof
By [L1], an invertible linear change of variables diagonalizes the constant principal matrix to , , , or the negative of one of these matrices, and multiplying the equation by a nonzero scalar removes the global sign.
By [L3], these three normal forms have discriminants , , and , and [L2] says that the sign of the discriminant is the invariant datum distinguishing them; therefore the principal part reduces to the Laplace, wave, or rank-one parabolic form listed in the statement.
Limits of the elliptic-parabolic-hyperbolic trichotomy
Remark
The names on this page combine related but differently formulated pointwise notions: ellipticity and strict hyperbolicity are conditions on real scalar second-order principal symbols, whereas the stated parabolic condition is for a space-time operator with a first-order time derivative and a semidefinite spatial quadratic form. These notions do not by themselves give a global taxonomy of equations that change type from point to point, systems with matrix-valued principal symbols, higher-order equations, or fully nonlinear equations whose leading behavior is not a single quadratic form.
Linear transport equations and their characteristic flow
Definition
Let be open and let , , and . The scalar first-order equation
is a linear transport equation.
Fix . A characteristic through is a solution of the ODE
on an interval containing . The corresponding space-time curve is . If these initial-value problems have unique solutions for varying initial data, write for the solution through . The resulting maps , on the domains where they are defined, form the characteristic flow of the transport field. Without uniqueness there are characteristics, but no single-valued characteristic flow.
A transport equation restricts to a linear ODE along each characteristic
Statement
Let satisfy
and let be a characteristic solving . Then
Facts & Assumptions
Given: A solution of the transport equation and a characteristic .
A linear transport equation and its characteristics are defined by the displayed PDE and ODE (Linear transport equations and their characteristic flow).
The total-derivative chain rule differentiates a composite by the gradient of the outer function applied to the derivative of the inner function (The chain rule for total derivatives: ).
Proof
Apply [L2] to the map ; since the space-time velocity of the characteristic is , this gives .
By [L1], the characteristic ODE gives , so substituting into step 1.1 and then using the PDE yields , which is the claimed scalar linear ODE along the characteristic.
Transport characteristics depend C^1 on the initial position
Statement
Let be on an open neighborhood of the graph of a characteristic solving
on a compact interval containing . Then, after shrinking to a neighborhood of , every initial point determines a characteristic on the same interval , the map is continuous on , and for each the map is . Its Jacobian matrix satisfies
Facts & Assumptions
Given: A transport field , a base characteristic on a compact interval , and nearby initial points .
Picard-Lindelof gives local existence and uniqueness for first-order systems (Picard-Lindelöf local existence and uniqueness for first-order systems).
Nearby ODE solutions exist on one common compact interval and depend continuously on the initial data (Continuous dependence of ODE solutions on initial data and parameters).
An ODE solution is equivalent to its Volterra integral equation (A first-order initial value problem is equivalent to its Volterra integral equation).
Gronwall's inequality turns an integral inequality into an exponential bound (Gronwall's integral inequality with variable and constant coefficients).
The norm of a vector integral is bounded by the integral of the norm (For and integrable when , ; for , is integrable).
The Jacobian matrix is the matrix of first partial derivatives with respect to the initial-position variables (The Jacobian matrix of partial derivatives and the gradient in the scalar-valued case).
Proof
By [L2] and [L3], after shrinking to a neighborhood of , every has a unique characteristic on the common compact interval , and is continuous there.
Fix a coordinate vector and a nonzero scalar with ; by [L4], the difference quotient satisfies , where , and because is on a compact neighborhood of the family of graphs, the matrices are uniformly bounded.
Apply [L6] to the integral equation in step 2.1. If , then ; if , rewrite step 2.1 as so . Gronwall therefore gives for every , uniformly in . The continuity from step 1.1 together with the uniform continuity of makes uniformly as ; comparing the equations for and on the forward or backward interval between and and applying [L5] again shows that is Cauchy in .
Let be the limit from step 3.1; passing to the limit in step 2.1 gives , which [L4] rewrites as with , and doing this for every coordinate vector while invoking [L7] identifies the matrix with ; therefore is and its Jacobian solves the displayed variational equation.
Homogeneous linear transport is solved by the inverse characteristic flow
Statement
Assume is regular enough that for each in a region the unique characteristic is defined from to and its whole segment remains in , and assume these characteristics have the usual flow consistency. If solves
then
Hence there is at most one classical solution on . Conversely, any function on satisfying this formula is that unique classical solution.
Facts & Assumptions
Given: A classical solution of the homogeneous transport equation on a region where every characteristic segment from time to time remains in , is unique, and satisfies flow consistency.
A transport equation and its characteristics are defined by the displayed PDE and ODE (Linear transport equations and their characteristic flow).
Along a characteristic, a transport solution satisfies the corresponding scalar ODE (A transport equation restricts to a linear ODE along each characteristic).
The chain rule computes the derivative of a function along a curve (The chain rule for total derivatives: ).
Proof
Fix and let be its characteristic. Its whole segment to time lies in , so [L2] with makes satisfy . Hence is constant and .
Step 1.1 proves the representation formula for every classical solution. The assumed characteristic flow is single valued, so two solutions with the same initial datum agree pointwise on .
Conversely, suppose a function satisfies the displayed formula. Along the characteristic through , flow consistency gives Applying the formula at therefore makes constant. By [L3] and the characteristic equation from [L1], its derivative is Evaluating at proves the PDE at , while setting gives the initial condition. Step 2.1 then gives uniqueness.
The inhomogeneous linear transport equation has the characteristic integrating-factor formula
Statement
Let be a region. Assume and , and assume that for each there is a unique characteristic on the closed interval with endpoints and , its space-time graph remains in , and the characteristic family has the usual flow consistency. If solves
then
Conversely, any function satisfying this formula solves the transport equation on .
Facts & Assumptions
Given: A transport field , continuous coefficients , a classical transport solution, and a unique flow-consistent characteristic through each whose whole segment to time remains in .
The chain rule computes the derivative of a function along a curve (The chain rule for total derivatives: ).
Along a characteristic, the PDE becomes the scalar linear ODE (A transport equation restricts to a linear ODE along each characteristic).
A scalar first-order linear ODE is solved by the integrating-factor formula (A scalar first-order linear ODE has a unique solution given by the integrating-factor formula).
A characteristic satisfies , and uniqueness makes the characteristic family a single-valued flow (Linear transport equations and their characteristic flow).
Proof
Fix and let . The characteristic segment stays in , so [L2] gives and .
If , the displayed formula is exactly the initial condition. If , apply [L3] to the scalar ODE from step 1.1 on the interval with endpoints and ; continuity of , the characteristic, and its in-domain graph makes the two composed coefficients continuous there, while oriented integrals cover either order of the endpoints. This yields the displayed formula for .
Conversely, suppose a function satisfies the displayed formula and fix the characteristic . Flow consistency gives for every relevant . Substitution in the displayed formula therefore writes exactly as the [L3] integrating-factor solution with coefficients and . Hence . By [L1] and [L4], , proving the PDE at . At , [L4] gives and the integral vanishes, so the formula also gives .
Noncharacteristic Cauchy surfaces for first-order transport
Definition
Let be the domain of the transport equation, and let be a parametrized hypersurface, written
For the transport equation , let the space-time transport vector be
The surface is noncharacteristic at when the matrix whose columns are the tangent vectors and the vector has nonzero determinant. Equivalently, is not tangent to the data surface.
A prescribed datum for the transport equation is a function , interpreted as the Cauchy condition .
Local linear transport has a unique solution from noncharacteristic Cauchy data
Statement
Let be near a point , and let be a parametrized hypersurface written , with datum of class . Assume . If is noncharacteristic at , then there are neighborhoods of and of and a unique such that
and
Facts & Assumptions
Given: coefficients, a data surface , datum , and a base point where the surface is noncharacteristic.
Noncharacteristic first-order data mean that the transport vector is transverse to the parametrized surface (Noncharacteristic Cauchy surfaces for first-order transport).
Characteristics depend on their initial position and satisfy the linearized variational equation (Transport characteristics depend C^1 on the initial position).
A scalar linear ODE with continuous coefficients has a unique solution given by its integrating-factor formula (A scalar first-order linear ODE has a unique solution given by the integrating-factor formula).
A map with invertible derivative at a point has a local inverse (The Euclidean inverse function theorem).
The chain rule computes the derivative of a function along a curve (The chain rule for total derivatives: ).
ODE solutions on one common compact interval depend jointly and continuously on their initial data and parameters (Continuous dependence of ODE solutions on initial data and parameters).
Proof
Apply [L2] to the space-time vector field with initial time . After shrinking near , its flow is jointly continuous, exists, and with . The coefficient is jointly continuous; applying [L6] to this linear matrix ODE, with as parameter, makes jointly continuous. Also is jointly continuous. Thus is in . Since is , is , and .
At , the -derivative of is the transport vector For each , the -derivative is because . Thus the columns of are exactly the tangent vectors to the data surface together with the transport vector, so [L1] says that is invertible.
By [L4], after shrinking domains there are neighborhoods of , of , and of such that is a diffeomorphism. Write . For each , [L3] gives the unique solution of namely The integrands on the compact local interval may be differentiated in and , so is . Define on .
Because , step 3.1 gives for . Moreover , so [L5] and give Substitution into the scalar ODE for proves the transport PDE throughout . If two solutions shared the data, [L5] would restrict each one to the same scalar IVP on every characteristic, and uniqueness in [L3] would make them agree throughout .
Support propagates along transport characteristics
Statement
Assume the homogeneous transport equation has a global flow map of spatial diffeomorphisms. If , then
for every time in the interval of existence.
Facts & Assumptions
Given: A global flow for the homogeneous transport equation and the representation .
Homogeneous linear transport is represented by the inverse characteristic flow (Homogeneous linear transport is solved by the inverse characteristic flow).
Proof
By [L1], , so exactly when , equivalently when and therefore when .
Because is a homeomorphism, it carries closures to closures, and taking closures in step 1.1 gives , so support can move only along the characteristic flow.
Characteristics are covectors before they are curves
For higher-order equations, characteristic objects are first defined by the principal symbol acting on covectors, hence on conormals to hypersurfaces. For first-order transport the transport vector field directly produces familiar base-space characteristic curves. Higher-order real principal symbols can also produce curves: their Hamilton vector fields define bicharacteristics in the cotangent bundle, whose projections are characteristic rays. Thus the transport curve picture is a special direct construction, while the general higher-order curve picture retains the covector variable.
5 · Examples, counterexamples and false statements
None yet.