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An orthonormal frame $\{e_a\}$ satisfies $g(e_a,e_b)={\eta }_{ab}$. Any two such frames differ by a local Lorentz transformation.

At any spacetime point, normal coordinates make the metric equal to the Minkowski metric and all Christoffel symbols vanish at that point.

The Riemann tensor measures the failure of covariant derivatives to commute.

A connection defines covariant differentiation by adding one connection term for each contravariant index and subtracting one for each covariant index.

Forthe only potentially nonzero Ricci component isThe vacuum equation is therefore the transverse Laplace equation.

A Killing vector field describes a direction in which geometry has a continuous symmetry: moving points together along its flow of a vector field preserves the metric tensor, including its pairing of tangent vectors. Such a symmetry gives a geodesic conserved quantity from a Killing vector, often a component of momentum or angular momentum. The direction may vary with position: rotation provides the basic example.

On a Riemannian manifold or Lorentzian manifold with metric tensor $g$, a smooth vector field $K$ is a Killing vector field when its Lie derivative of $g$ vanishes. If ${\Phi }_s$ is its local flow of a vector field, the equivalent statements areThe middle equality uses the pullback of a covariant tensor: the local flow maps are isometries on their domains. The last equality is the Killing equation, using the Levi-Civita connection. The infinitesimal condition holds throughout an open domain and integrates to exact local isometries; it is stronger than making the first derivatives of $g$ vanish at a single point. See Tong, section 4.3.1, equation (4.29).

For example, on the interval $0<x<1$ with Euclidean metric $d{x}^2$, $K={\partial }_x$ translates points by $x\mapsto x+s$. This is a local isometry, but a point reaches the end of the interval in finite flow parameter. A local flow of a vector field can be incomplete; see Killing fields on incomplete manifolds need not generate global isometries. The full-plane translation and rotation examples under Euclidean Killing vector field instead have flows for every real $s$.

Model theory studies mathematical structures through the first-order sentences and formulas they satisfy.

Category theory studies mathematical structures through the morphisms between them. A category specifies which objects of a category and arrows are allowed; a functor translates these objects and arrows into another category; a natural transformation compares two such translations coherently. For example, the category of groups records both groups and group homomorphisms, so it remembers which maps respect multiplication.

The usefulness appears in constructions we already know. A Cartesian product combines two sets, while a direct product of groups combines two groups. Both satisfy the same universal property of a categorical product: any pair of maps into the factors comes from exactly one map into the product. This gives a reusable way to construct maps and establish isomorphisms, without depending on a particular choice of coordinates or element labels. In a partially ordered set regarded as a category, the corresponding categorical product is a greatest lower bound. The shared definition exposes a connection between pairing data and finding the best common bound.

Start with the computed examples under category, functor, natural transformation, and product in a category. Their arrow equations are practical tests: do both routes in a commutative diagram produce the same result? Later, the same language organizes tensor products, adjunctions, and the fundamental group of a topological space. Emily Riehl's textbook develops these basic notions before using them to compare constructions across different subjects. ^[ref]

In their 1945 paper, Samuel Eilenberg and Saunders Mac Lane set out the notions of category, functor, and natural transformation. The introduction uses the relation between a vector space and its dual space to ask which comparisons are natural, setting up a precise language for compatibility with maps. ^[ref]

A mathematical proof is a finite deductive argument from stated assumptions to a conclusion. It explains why the conclusion follows for every instance covered by its hypotheses. An informal mathematical proof can omit routine detail while making its assumptions and deductions clear; a formal proof expresses them in a specified formal language with specified inference rules. The distinction between assumptions, propositions, and deductions is introduced in Leighton and Rubinfeld, What is a proof?, sections 1–3.

Checking examples can suggest a pattern, but need not establish a universally quantified claim. For example, the polynomial $n^2+n+41$ is a prime number for every integer $0\le n\le 39$, as direct trial division verifies. At $n=40$ it equals $1681=41^2$, a composite number. Those first forty successes do not prove the claim for all natural numbers; one counterexample disproves it. The point is not to discard computation: it can find a conjecture, detect a mistake, or verify a finite exhaustive case analysis. The question is whether the cases checked cover the statement being claimed.

For a positive example, the sum of two even integers is even: writing them as $2a$ and $2b$ with $a,b\in \mathbb{Z}$ gives $2a+2b=2(a+b)$, and $a+b$ is an integer. This direct argument handles every allowed pair at once. A useful mathematical proof therefore does more than certify an answer: it exposes the mechanism and the exact assumptions that make the answer transferable.

Statistical learning theory studies mathematical guarantees and limitations for learning from data, including how a hypothesis class, loss function, and sample size affect generalization. It distinguishes a particular fitted rule's prediction error from the expected performance of the learning procedure across possible training data.

Graph theory studies vertices and the edges connecting them: which graph paths exist, how efficiently we can travel, and which connections are indispensable. A road map becomes a graph when junctions are vertices and road segments are edges; a communications network uses routers and links instead. The drawing helps us see connections, but its apparent lengths need not be the costs being optimized. Record travel times or transmission costs separately in a weighted graph.

The appeal of graph theory is that one model serves many practical questions. Graph paths describe routes, connected components of a graph identify isolated regions, and minimum spanning trees connect all locations cheaply. The shortest path problem asks a different question: find the cheapest journey between specified locations. Our four-vertex example below makes that distinction concrete and shows how the Dijkstra algorithm discovers a route without trying every graph path.

Mathematical logic studies formal languages, formal proofs, model theory, computability theory, and the foundations of mathematics. It makes the rules of reasoning explicit enough that reasoning itself becomes an object of mathematics. Proof theory studies deductions; model theory studies interpretations in which statements hold.

Three questions organize the subject: is an expression a well-formed first-order formula, is it true in a specified first-order structure, and does it have a formal proof from specified axioms? These questions are distinct. For example, $\exists n(n+n=1)$ is a first-order sentence in an appropriate first-order language; it is false in the natural numbers but true in the rational numbers, with $n=1/2$.

The beauty of mathematical logic is that the same logical connectives and rules of universal quantification and existential quantification organize arguments across mathematics. Classical propositional logic uses two truth values; Intuitionistic propositional logic interprets logical implication through constructions on proofs, without unrestricted law of excluded middle. This proof-based framework is developed in Frank Pfenning, Natural Deduction, sections 1–3.

Set theory studies sets, their set elements, and the constructions and sizes possible for them. It supplies a shared vocabulary for mathematics: a function connects two sets, a binary relation specifies which pairs of objects are related, and a probability space starts with a set of possible outcomes. Giving these objects a common language lets a construction from one subject be used in another without repeating its underlying bookkeeping.

A particularly beautiful question is how to compare sizes without first assigning a number. Two sets have the same cardinality when a bijection pairs every member of one with exactly one member of the other. For finite sets, this agrees with ordinary counting. For infinite sets, it produces surprises: $n\mapsto 2n$ bijects the natural numbers with the nonnegative even integers, and the Cantor pairing function bijects the Cartesian product ${\mathbb{N}}^2$ with $\mathbb{N}$. A proper subset can have the same cardinality as its containing set.

This does not make all infinite sets equally large. Cantor's diagonal argument shows that all infinite binary sequences cannot be listed by the natural numbers. Cantor's theorem gives the wider principle: the power set of any set has strictly greater cardinality. The contrast between an explicit enumeration and an impossibility argument makes the meaning of infinity much sharper than a picture of something merely continuing forever.

Set formation itself requires care. Russell's paradox shows why not every condition defines a set of all objects satisfying it. Zermelo–Fraenkel set theory instead uses axioms specifying permitted constructions, including set comprehension within a previously given set. The restrictions are useful: they let us form the collections ordinary arguments need while blocking the unrestricted construction that produces the paradox.

A non-Newtonian fluid has a stress--strain-rate relation with nonlinear, time-dependent, history-dependent, or microstructure-dependent behavior.

Dispersion of a solute by flow combines molecular diffusion with spatially varying advection, often producing an enhanced large-scale longitudinal diffusivity.

Geophysical fluid dynamics studies fluid motion on rotating, stratified planets, including oceans, atmospheres, cores, and planetary interiors.

Astrophysical fluid dynamics studies gases, plasmas, stars, discs, and outflows under gravity, rotation, compressibility, and magnetic fields.

Linear acoustics describes small pressure, density, and velocity perturbations about a uniform quiescent compressible fluid.