The Fundamental Theorem of Calculus
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A Brief Tour of Vector Calculus
A BRIEF TOUR OF VECTOR CALCULUS A. HAVENS Contents 0 Prelude ii 1 Directional Derivatives, the Gradient and the Del Operator 1 1.1 Conceptual Review: Directional Derivatives and the Gradient........... 1 1.2 The Gradient as a Vector Field............................ 5 1.3 The Gradient Flow and Critical Points ....................... 10 1.4 The Del Operator and the Gradient in Other Coordinates*............ 17 1.5 Problems........................................ 21 2 Vector Fields in Low Dimensions 26 2 3 2.1 General Vector Fields in Domains of R and R . 26 2.2 Flows and Integral Curves .............................. 31 2.3 Conservative Vector Fields and Potentials...................... 32 2.4 Vector Fields from Frames*.............................. 37 2.5 Divergence, Curl, Jacobians, and the Laplacian................... 41 2.6 Parametrized Surfaces and Coordinate Vector Fields*............... 48 2.7 Tangent Vectors, Normal Vectors, and Orientations*................ 52 2.8 Problems........................................ 58 3 Line Integrals 66 3.1 Defining Scalar Line Integrals............................. 66 3.2 Line Integrals in Vector Fields ............................ 75 3.3 Work in a Force Field................................. 78 3.4 The Fundamental Theorem of Line Integrals .................... 79 3.5 Motion in Conservative Force Fields Conserves Energy .............. 81 3.6 Path Independence and Corollaries of the Fundamental Theorem......... 82 3.7 Green's Theorem.................................... 84 3.8 Problems........................................ 89 4 Surface Integrals, Flux, and Fundamental Theorems 93 4.1 Surface Integrals of Scalar Fields........................... 93 4.2 Flux........................................... 96 4.3 The Gradient, Divergence, and Curl Operators Via Limits* . 103 4.4 The Stokes-Kelvin Theorem..............................108 4.5 The Divergence Theorem ...............................112 4.6 Problems........................................114 List of Figures 117 i 11/14/19 Multivariate Calculus: Vector Calculus Havens 0. -
The Infinite and Contradiction: a History of Mathematical Physics By
The infinite and contradiction: A history of mathematical physics by dialectical approach Ichiro Ueki January 18, 2021 Abstract The following hypothesis is proposed: \In mathematics, the contradiction involved in the de- velopment of human knowledge is included in the form of the infinite.” To prove this hypothesis, the author tries to find what sorts of the infinite in mathematics were used to represent the con- tradictions involved in some revolutions in mathematical physics, and concludes \the contradiction involved in mathematical description of motion was represented with the infinite within recursive (computable) set level by early Newtonian mechanics; and then the contradiction to describe discon- tinuous phenomena with continuous functions and contradictions about \ether" were represented with the infinite higher than the recursive set level, namely of arithmetical set level in second or- der arithmetic (ordinary mathematics), by mechanics of continuous bodies and field theory; and subsequently the contradiction appeared in macroscopic physics applied to microscopic phenomena were represented with the further higher infinite in third or higher order arithmetic (set-theoretic mathematics), by quantum mechanics". 1 Introduction Contradictions found in set theory from the end of the 19th century to the beginning of the 20th, gave a shock called \a crisis of mathematics" to the world of mathematicians. One of the contradictions was reported by B. Russel: \Let w be the class [set]1 of all classes which are not members of themselves. Then whatever class x may be, 'x is a w' is equivalent to 'x is not an x'. Hence, giving to x the value w, 'w is a w' is equivalent to 'w is not a w'."[52] Russel described the crisis in 1959: I was led to this contradiction by Cantor's proof that there is no greatest cardinal number. -
Part IA — Vector Calculus
Part IA | Vector Calculus Based on lectures by B. Allanach Notes taken by Dexter Chua Lent 2015 These notes are not endorsed by the lecturers, and I have modified them (often significantly) after lectures. They are nowhere near accurate representations of what was actually lectured, and in particular, all errors are almost surely mine. 3 Curves in R 3 Parameterised curves and arc length, tangents and normals to curves in R , the radius of curvature. [1] 2 3 Integration in R and R Line integrals. Surface and volume integrals: definitions, examples using Cartesian, cylindrical and spherical coordinates; change of variables. [4] Vector operators Directional derivatives. The gradient of a real-valued function: definition; interpretation as normal to level surfaces; examples including the use of cylindrical, spherical *and general orthogonal curvilinear* coordinates. Divergence, curl and r2 in Cartesian coordinates, examples; formulae for these oper- ators (statement only) in cylindrical, spherical *and general orthogonal curvilinear* coordinates. Solenoidal fields, irrotational fields and conservative fields; scalar potentials. Vector derivative identities. [5] Integration theorems Divergence theorem, Green's theorem, Stokes's theorem, Green's second theorem: statements; informal proofs; examples; application to fluid dynamics, and to electro- magnetism including statement of Maxwell's equations. [5] Laplace's equation 2 3 Laplace's equation in R and R : uniqueness theorem and maximum principle. Solution of Poisson's equation by Gauss's method (for spherical and cylindrical symmetry) and as an integral. [4] 3 Cartesian tensors in R Tensor transformation laws, addition, multiplication, contraction, with emphasis on tensors of second rank. Isotropic second and third rank tensors. -
Or, If the Sine Functions Be Eliminated by Means of (11)
52 BINOMIAL THEOREM AND NEWTONS MONUMENT. [Nov., or, if the sine functions be eliminated by means of (11), e <*i : <** : <xz = X^ptptf : K(P*ptf : A3(Pi#*) . (53) While (52) does not enable us to construct the point of least attraction, it furnishes a solution of the converse problem : to determine the ratios of the masses of three points so as to make the sum of their attractions on a point P within their triangle a minimum. If, in (50), we put n = 2 and ax + a% = 1, and hence pt + p% = 1, this equation can be regarded as that of a curve whose ordinate s represents the sum of the attractions exerted by the points et and e2 on the foot of the ordinate. This curve approaches asymptotically the perpendiculars erected on the vector {ex — e2) at ex and e% ; and the point of minimum attraction corresponds to its lowest point. Similarly, in the case n = 3, the sum of the attractions exerted by the vertices of the triangle on any point within this triangle can be rep resented by the ordinate of a surface, erected at this point at right angles to the plane of the triangle. This suggestion may here suffice. 22. Concluding remark.—Further results concerning gen eralizations of the problem of the minimum sum of distances are reserved for a future communication. WAS THE BINOMIAL THEOEEM ENGKAVEN ON NEWTON'S MONUMENT? BY PKOFESSOR FLORIAN CAJORI. Moritz Cantor, in a recently published part of his admir able work, Vorlesungen über Gescliichte der Mathematik, speaks of the " Binomialreihe, welcher man 1727 bei Newtons Tode . -
Generalized Stokes' Theorem
Chapter 4 Generalized Stokes’ Theorem “It is very difficult for us, placed as we have been from earliest childhood in a condition of training, to say what would have been our feelings had such training never taken place.” Sir George Stokes, 1st Baronet 4.1. Manifolds with Boundary We have seen in the Chapter 3 that Green’s, Stokes’ and Divergence Theorem in Multivariable Calculus can be unified together using the language of differential forms. In this chapter, we will generalize Stokes’ Theorem to higher dimensional and abstract manifolds. These classic theorems and their generalizations concern about an integral over a manifold with an integral over its boundary. In this section, we will first rigorously define the notion of a boundary for abstract manifolds. Heuristically, an interior point of a manifold locally looks like a ball in Euclidean space, whereas a boundary point locally looks like an upper-half space. n 4.1.1. Smooth Functions on Upper-Half Spaces. From now on, we denote R+ := n n f(u1, ... , un) 2 R : un ≥ 0g which is the upper-half space of R . Under the subspace n n n topology, we say a subset V ⊂ R+ is open in R+ if there exists a set Ve ⊂ R open in n n n R such that V = Ve \ R+. It is intuitively clear that if V ⊂ R+ is disjoint from the n n n subspace fun = 0g of R , then V is open in R+ if and only if V is open in R . n n Now consider a set V ⊂ R+ which is open in R+ and that V \ fun = 0g 6= Æ. -
LECTURE 27: the LAPLAST ONE Monday, November 25, 2019 5:23 PM
LECTURE 27: THE LAPLAST ONE Monday, November 25, 2019 5:23 PM Today: Three little remaining topics related to Laplace I- 3 DIMENSIONS Previously: Solved Laplace's equation in 2 dimensions by converting it into polar coordinates. The same idea works in 3 dimensions if you use spherical coordinates. 3 Suppose u = u(x,y,z) solves u xx + u yy + u zz = 0 in R Using spherical coordinates, you eventually get urr + 2 ur + JUNK = 0 r (where r = x 2 + y 2 + z 2 and JUNK doesn't depend on r) If we're looking for radial solutions, we set JUNK = 0 Get u rr + 2 u r = 0 which you can solve to get r u(x,y,z) = -C + C' solves u xx + u yy + u zz = 0 r Finally, setting C = -1/(4π) and C' = 0, you get Fundamental solution of Laplace for n = 3 S(x,y,z) = 1 = 4πr 4π x 2 + y 2 + z 2 Note: In n dimensions, get u rr + n-1 ur = 0 r => u( r ) = -C + C' rn-2 => S(x) = Blah (for some complicated Blah) rn-1 Why fundamental? Because can build up other solutions from this! Fun Fact: A solution of -Δ u = f (Poisson's equation) in R n is u(x) = S(x) * f(x) = S(x -y) f(y) dy (Basically the constant is chosen such that -ΔS = d0 <- Dirac at 0) II - DERIVATION OF LAPLACE Two goals: Derive Laplace's equation, and also highlight an important structure of Δu = 0 A) SETTING n Definition: If F = (F 1, …, F n) is a vector field in R , then div(F) = (F 1)x1 + … + (F n)xn Notice : If u = u(x 1, …, x n), then u = (u x1 , …, u xn ) => div( u) = (u x1 )x1 + … + (u xn )xn = u x1 x1 + … + u xn xn = Δu Fact: Δu = div( u) "divergence structure" In particular, Laplace's equation works very well with the divergence theorem Divergence Theorem: F n dS = div(F) dx bdy D D B) DERIVATION Suppose you have a fluid F that is in equilibrium (think F = temperature or chemical concentration) Equilibrium means that for any region D, the net flux of F is 0. -
Green Gauss Theorem Yesterday, We Continued Discussions on Index Notations That Can Be Used to Represent Tensors
15/02/2017 LECTURE – 15 Green Gauss Theorem Yesterday, we continued discussions on index notations that can be used to represent tensors. In index notations we saw the Gradient of a scalar Divergence of a vector Cross product of vector etc. We also saw that a second rank tensor can be written as a sum of a symmetric & asymmetric tensor. 11 i.e, BBBBB[][] ij22 ij ji ij ji First part is symmetric & second part is asymmetric Today we will briefly discuss about Green Gauss Theorem etc. Green Gauss Theorem relates the volume & surface integrals. The most common form of Gauss’s theorem is the Gauss divergence theorem which you have studied in previous courses. 푛 푣 ds If you have a volume U formed by surface S, the Gauss divergence theorem for a vector v suggest that v.. ndSˆ vdU SU where, S = bounding surface on closed surface U = volumetric domain nˆ = the unit outward normal vector of the elementary surface area ds This Gauss Divergence theorem can also be represented using index notations. v v n dS i dU ii SUxi For any scalar multiple or factor for vector v , say v , the Green Gauss can be represented as (v . nˆ ) dS .( v ) dU SU i.e. (v . nˆ ) dS ( . v ) dU ( v . ) dU SUU v or v n dSi dU v dU i i i SUUxxii Gauss’s theorem is applicable not only to a vector . It can be applied to any tensor B (say) B B n dSij dU B dU ij i ij SUUxxii Stoke’s Theorem Stoke’s theorem relates the integral over an open surface S, to line integral around the surface’s bounding curve (say C) You need to appropriately choose the unit outward normal vector to the surface 푛 푡 dr v. -
Vector Calculus Applicationsž 1. Introduction 2. the Heat Equation
Vector Calculus Applications 1. Introduction The divergence and Stokes’ theorems (and their related results) supply fundamental tools which can be used to derive equations which can be used to model a number of physical situations. Essentially, these theorems provide a mathematical language with which to express physical laws such as conservation of mass, momentum and energy. The resulting equations are some of the most fundamental and useful in engineering and applied science. In the following sections the derivation of some of these equations will be outlined. The goal is to show how vector calculus is used in applications. Generally speaking, the equations are derived by first using a conservation law in integral form, and then converting the integral form to a differential equation form using the divergence theorem, Stokes’ theorem, and vector identities. The differential equation forms tend to be easier to work with, particularly if one is interested in solving such equations, either analytically or numerically. 2. The Heat Equation Consider a solid material occupying a region of space V . The region has a boundary surface, which we shall designate as S. Suppose the solid has a density and a heat capacity c. If the temperature of the solid at any point in V is T.r; t/, where r x{ y| zkO is the position vector (so that T depends upon x, y, z and t), then theE total heatE eergyD O containedC O C in the solid is • cT dV : V Heat energy can get in or out of the region V by flowing across the boundary S, or it can be generated inside V . -
3D Topological Quantum Computing
3D Topological Quantum Computing Torsten Asselmeyer-Maluga German Aerospace Center (DLR), Rosa-Luxemburg-Str. 2 10178 Berlin, Germany [email protected] July 20, 2021 Abstract In this paper we will present some ideas to use 3D topology for quan- tum computing extending ideas from a previous paper. Topological quan- tum computing used “knotted” quantum states of topological phases of matter, called anyons. But anyons are connected with surface topology. But surfaces have (usually) abelian fundamental groups and therefore one needs non-abelian anyons to use it for quantum computing. But usual materials are 3D objects which can admit more complicated topologies. Here, complements of knots do play a prominent role and are in principle the main parts to understand 3-manifold topology. For that purpose, we will construct a quantum system on the complements of a knot in the 3-sphere (see arXiv:2102.04452 for previous work). The whole system is designed as knotted superconductor where every crossing is a Josephson junction and the qubit is realized as flux qubit. We discuss the proper- ties of this systems in particular the fluxion quantization by using the A-polynomial of the knot. Furthermore we showed that 2-qubit opera- tions can be realized by linked (knotted) superconductors again coupled via a Josephson junction. 1 Introduction Quantum computing exploits quantum-mechanical phenomena such as super- position and entanglement to perform operations on data, which in many cases, arXiv:2107.08049v1 [quant-ph] 16 Jul 2021 are infeasible to do efficiently on classical computers. Topological quantum computing seeks to implement a more resilient qubit by utilizing non-Abelian forms of matter like non-abelian anyons to store quantum information. -
50 Mathematical Ideas You Really Need to Know
50 mathematical ideas you really need to know Tony Crilly 2 Contents Introduction 01 Zero 02 Number systems 03 Fractions 04 Squares and square roots 05 π 06 e 07 Infinity 08 Imaginary numbers 09 Primes 10 Perfect numbers 11 Fibonacci numbers 12 Golden rectangles 13 Pascal’s triangle 14 Algebra 15 Euclid’s algorithm 16 Logic 17 Proof 3 18 Sets 19 Calculus 20 Constructions 21 Triangles 22 Curves 23 Topology 24 Dimension 25 Fractals 26 Chaos 27 The parallel postulate 28 Discrete geometry 29 Graphs 30 The four-colour problem 31 Probability 32 Bayes’s theory 33 The birthday problem 34 Distributions 35 The normal curve 36 Connecting data 37 Genetics 38 Groups 4 39 Matrices 40 Codes 41 Advanced counting 42 Magic squares 43 Latin squares 44 Money mathematics 45 The diet problem 46 The travelling salesperson 47 Game theory 48 Relativity 49 Fermat’s last theorem 50 The Riemann hypothesis Glossary Index 5 Introduction Mathematics is a vast subject and no one can possibly know it all. What one can do is explore and find an individual pathway. The possibilities open to us here will lead to other times and different cultures and to ideas that have intrigued mathematicians for centuries. Mathematics is both ancient and modern and is built up from widespread cultural and political influences. From India and Arabia we derive our modern numbering system but it is one tempered with historical barnacles. The ‘base 60’ of the Babylonians of two or three millennia BC shows up in our own culture – we have 60 seconds in a minute and 60 minutes in an hour; a right angle is still 90 degrees and not 100 grads as revolutionary France adopted in a first move towards decimalization. -
The Divergence Theorem.Doc 1/2
9/16/2005 The Divergence Theorem.doc 1/2 The Divergence Theorem Recall we studied volume integrals of the form: ∫∫∫ g (rdv) V It turns out that any and every scalar field can be written as the divergence of some vector field, i.e.: g (rr) = ∇⋅A( ) Therefore we can equivalently write any volume integral as: ∫∫∫ ∇⋅A (r )dv V The divergence theorem states that this integral is equal to: ∫∫∫∇⋅AA(rr)dv =w ∫∫ ( ) ⋅ ds VS where S is the closed surface that completely surrounds volume V, and vector ds points outward from the closed surface. For example, if volume V is a sphere, then S is the surface of that sphere. The divergence theorem states that the volume integral of a scalar field can be likewise evaluated as a surface integral of a vector field! Jim Stiles The Univ. of Kansas Dept. of EECS 9/16/2005 The Divergence Theorem.doc 2/2 What the divergence theorem indicates is that the total “divergence” of a vector field through the surface of any volume is equal to the sum (i.e., integration) of the divergence at all points within the volume. 4 2 0 -2 -4 -4 - 2 0 2 4 In other words, if the vector field is diverging from some point in the volume, it must simultaneously be converging to another adjacent point within the volume—the net effect is therefore zero! Thus, the only values that make any difference in the volume integral are the divergence or convergence of the vector field across the surface surrounding the volume—vectors that will be converging or diverging to adjacent points outside the volume (across the surface) from points inside the volume. -
Differential Forms: Unifying the Theorems of Vector Calculus
Differential Forms: Unifying the Theorems of Vector Calculus In class we have discussed the important vector calculus theorems known as Green's Theorem, Divergence Theorem, and Stokes's Theorem. Interestingly enough, all of these results, as well as the fundamental theorem for line integrals (so in particular the fundamental theorem of calculus), are merely special cases of one and the same theorem named after George Gabriel Stokes (1819-1903). This all-including theorem is stated in terms of differential forms. Without giving exact definitions, let us use the language of differential forms to unify the theorems we have learned. A striking pattern will emerge. 0-forms. A scalar field (i.e. a real-valued function) is also called a 0-form. 1-forms. Recall the following notation for line integrals (in 3-space, say): Z Z b Z F(r) · dr = P x0(t)dt +Q y0(t)dt +R z0(t)dt = P dx + Qdy + Rdz; C a | {z } | {z } | {z } C dx dy dz where F = P i + Qj + Rk. The expression P (x; y; z)dx + Q(x; y; z)dy + R(x; y; z)dz is called a 1-form. 2-forms. In evaluating surface integrals we can introduce similar notation: ZZ F · n dS S ZZ @r × @r @r @r @u @v = F · × dudv Γ @r @r @u @v @u × @v ZZ @y @y ZZ @x @x ZZ @x @x @u @v @u @v @u @v = P @z @z dudv − Q @z @z dudv + R @y @y dudv Γ @u @v Γ @u @v Γ @u @v | {z } | {z } | {z } dy^dz dx^dz dx^dy ZZ = P dy ^ dz − Qdx ^ dz + Rdx ^ dy: S We call P (x; y; z)dy ^ dz − Q(x; y; z)dx ^ dz + R(x; y; z)dx ^ dy a 2-form.