The Early History of Partial Differential Equations and of Partial Differentiation and Integration
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Lecture 11 Line Integrals of Vector-Valued Functions (Cont'd)
Lecture 11 Line integrals of vector-valued functions (cont’d) In the previous lecture, we considered the following physical situation: A force, F(x), which is not necessarily constant in space, is acting on a mass m, as the mass moves along a curve C from point P to point Q as shown in the diagram below. z Q P m F(x(t)) x(t) C y x The goal is to compute the total amount of work W done by the force. Clearly the constant force/straight line displacement formula, W = F · d , (1) where F is force and d is the displacement vector, does not apply here. But the fundamental idea, in the “Spirit of Calculus,” is to break up the motion into tiny pieces over which we can use Eq. (1 as an approximation over small pieces of the curve. We then “sum up,” i.e., integrate, over all contributions to obtain W . The total work is the line integral of the vector field F over the curve C: W = F · dx . (2) ZC Here, we summarize the main steps involved in the computation of this line integral: 1. Step 1: Parametrize the curve C We assume that the curve C can be parametrized, i.e., x(t)= g(t) = (x(t),y(t), z(t)), t ∈ [a, b], (3) so that g(a) is point P and g(b) is point Q. From this parametrization we can compute the velocity vector, ′ ′ ′ ′ v(t)= g (t) = (x (t),y (t), z (t)) . (4) 1 2. Step 2: Compute field vector F(g(t)) over curve C F(g(t)) = F(x(t),y(t), z(t)) (5) = (F1(x(t),y(t), z(t), F2(x(t),y(t), z(t), F3(x(t),y(t), z(t))) , t ∈ [a, b] . -
Notes on Partial Differential Equations John K. Hunter
Notes on Partial Differential Equations John K. Hunter Department of Mathematics, University of California at Davis Contents Chapter 1. Preliminaries 1 1.1. Euclidean space 1 1.2. Spaces of continuous functions 1 1.3. H¨olderspaces 2 1.4. Lp spaces 3 1.5. Compactness 6 1.6. Averages 7 1.7. Convolutions 7 1.8. Derivatives and multi-index notation 8 1.9. Mollifiers 10 1.10. Boundaries of open sets 12 1.11. Change of variables 16 1.12. Divergence theorem 16 Chapter 2. Laplace's equation 19 2.1. Mean value theorem 20 2.2. Derivative estimates and analyticity 23 2.3. Maximum principle 26 2.4. Harnack's inequality 31 2.5. Green's identities 32 2.6. Fundamental solution 33 2.7. The Newtonian potential 34 2.8. Singular integral operators 43 Chapter 3. Sobolev spaces 47 3.1. Weak derivatives 47 3.2. Examples 47 3.3. Distributions 50 3.4. Properties of weak derivatives 53 3.5. Sobolev spaces 56 3.6. Approximation of Sobolev functions 57 3.7. Sobolev embedding: p < n 57 3.8. Sobolev embedding: p > n 66 3.9. Boundary values of Sobolev functions 69 3.10. Compactness results 71 3.11. Sobolev functions on Ω ⊂ Rn 73 3.A. Lipschitz functions 75 3.B. Absolutely continuous functions 76 3.C. Functions of bounded variation 78 3.D. Borel measures on R 80 v vi CONTENTS 3.E. Radon measures on R 82 3.F. Lebesgue-Stieltjes measures 83 3.G. Integration 84 3.H. Summary 86 Chapter 4. -
MULTIVARIABLE CALCULUS (MATH 212 ) COMMON TOPIC LIST (Approved by the Occidental College Math Department on August 28, 2009)
MULTIVARIABLE CALCULUS (MATH 212 ) COMMON TOPIC LIST (Approved by the Occidental College Math Department on August 28, 2009) Required Topics • Multivariable functions • 3-D space o Distance o Equations of Spheres o Graphing Planes Spheres Miscellaneous function graphs Sections and level curves (contour diagrams) Level surfaces • Planes o Equations of planes, in different forms o Equation of plane from three points o Linear approximation and tangent planes • Partial Derivatives o Compute using the definition of partial derivative o Compute using differentiation rules o Interpret o Approximate, given contour diagram, table of values, or sketch of sections o Signs from real-world description o Compute a gradient vector • Vectors o Arithmetic on vectors, pictorially and by components o Dot product compute using components v v v v v ⋅ w = v w cosθ v v v v u ⋅ v = 0⇔ u ⊥ v (for nonzero vectors) o Cross product Direction and length compute using components and determinant • Directional derivatives o estimate from contour diagram o compute using the lim definition t→0 v v o v compute using fu = ∇f ⋅ u ( u a unit vector) • Gradient v o v geometry (3 facts, and understand how they follow from fu = ∇f ⋅ u ) Gradient points in direction of fastest increase Length of gradient is the directional derivative in that direction Gradient is perpendicular to level set o given contour diagram, draw a gradient vector • Chain rules • Higher-order partials o compute o mixed partials are equal (under certain conditions) • Optimization o Locate and -
Understanding Binomial Sequential Testing Table of Contents of Two START Sheets
Selected Topics in Assurance Related Technologies START Volume 12, Number 2 Understanding Binomial Sequential Testing Table of Contents of two START sheets. In this first START Sheet, we start by exploring double sampling plans. From there, we proceed to • Introduction higher dimension sampling plans, namely sequential tests. • Double Sampling (Two Stage) Testing Procedures We illustrate their discussion via numerical and practical examples of sequential tests for attributes (pass/fail) data that • The Sequential Probability Ratio Test (SPRT) follow the Binomial distribution. Such plans can be used for • The Average Sample Number (ASN) Quality Control as well as for Life Testing problems. We • Conclusions conclude with a discussion of the ASN or “expected sample • For More Information number,” a performance measure widely used to assess such multi-stage sampling plans. In the second START Sheet, we • About the Author will discuss sequential testing plans for continuous data (vari- • Other START Sheets Available ables), following the same scheme used herein. Introduction Double Sampling (Two Stage) Testing Determining the sample size “n” required in testing and con- Procedures fidence interval (CI) derivation is of importance for practi- tioners, as evidenced by the many related queries that we In hypothesis testing, we often define a Null (H0) that receive at the RAC in this area. Therefore, we have expresses the desired value for the parameter under test (e.g., addressed this topic in a number of START sheets. For exam- acceptable quality). Then, we define the Alternative (H1) as ple, we discussed the problem of calculating the sample size the unacceptable value for such parameter. -
Reading Inventory Technical Guide
Technical Guide Using a Valid and Reliable Assessment of College and Career Readiness Across Grades K–12 Technical Guide Excepting those parts intended for classroom use, no part of this publication may be reproduced in whole or in part, or stored in a retrieval system, or transmitted in any form or by any means, electronic, mechanical, photocopying, recording, or otherwise, without written permission of the publisher. For information regarding permission, write to Scholastic Inc., 557 Broadway, New York, NY 10012. Scholastic Inc. grants teachers who have purchased SRI College & Career permission to reproduce from this book those pages intended for use in their classrooms. Notice of copyright must appear on all copies of copyrighted materials. Portions previously published in: Scholastic Reading Inventory Target Success With the Lexile Framework for Reading, copyright © 2005, 2003, 1999; Scholastic Reading Inventory Using the Lexile Framework, Technical Manual Forms A and B, copyright © 1999; Scholastic Reading Inventory Technical Guide, copyright © 2007, 2001, 1999; Lexiles: A System for Measuring Reader Ability and Text Difficulty, A Guide for Educators, copyright © 2008; iRead Screener Technical Guide by Richard K. Wagner, copyright © 2014; Scholastic Inc. Copyright © 2014, 2008, 2007, 1999 by Scholastic Inc. All rights reserved. Published by Scholastic Inc. ISBN-13: 978-0-545-79638-5 ISBN-10: 0-545-79638-5 SCHOLASTIC, READ 180, SCHOLASTIC READING COUNTS!, and associated logos are trademarks and/or registered trademarks of Scholastic Inc. LEXILE and LEXILE FRAMEWORK are registered trademarks of MetaMetrics, Inc. Other company names, brand names, and product names are the property and/or trademarks of their respective owners. -
A Rhetorical Analysis of Sir Isaac Newton's Principia A
A RHETORICAL ANALYSIS OF SIR ISAAC NEWTON’S PRINCIPIA A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF ARTS IN THE GRADUATE SCHOOL OF THE TEXAS WOMAN’S UNIVERSITY DEPARTMENT OF ENGLISH, SPEECH, AND FOREIGN LANGUAGES COLLEGE OF ARTS AND SCIENCES BY GIRIBALA JOSHI, B.S., M.S. DENTON, TEXAS AUGUST 2018 Copyright © 2018 by Giribala Joshi DEDICATION Nature and Nature’s Laws lay hid in Night: God said, “Let Newton be!” and all was light. ~ Alexander Pope Dedicated to all the wonderful eighteenth-century Enlightenment thinkers and philosophers! ii ACKNOWLEDGMENTS I would like to acknowledge the continuous support and encouragement that I received from the Department of English, Speech and Foreign Languages. I especially want to thank my thesis committee member Dr. Ashley Bender, and my committee chair Dr. Brian Fehler, for their guidance and feedback while writing this thesis. iii ABSTRACT GIRIBALA JOSHI A RHETORICAL ANALYSIS OF SIR ISAAC NEWTON’S PRINCIPIA AUGUST 2018 In this thesis, I analyze Isaac Newton's Philosophiae Naturalis Principia Mathematica in the framework of Aristotle’s theories of rhetoric. Despite the long-held view that science only deals with brute facts and does not require rhetoric, we learn that science has its own special topics. This study highlights the rhetorical situation of the Principia and Newton’s rhetorical strategies, emphasizing the belief that scientific facts and theories are also rhetorical constructions. This analysis shows that the credibility of the author and the text, the emotional debates before and after the publication of the text, the construction of logical arguments, and the presentation style makes the book the epitome of scientific writing. -
The Newton-Leibniz Controversy Over the Invention of the Calculus
The Newton-Leibniz controversy over the invention of the calculus S.Subramanya Sastry 1 Introduction Perhaps one the most infamous controversies in the history of science is the one between Newton and Leibniz over the invention of the infinitesimal calculus. During the 17th century, debates between philosophers over priority issues were dime-a-dozen. Inspite of the fact that priority disputes between scientists were ¡ common, many contemporaries of Newton and Leibniz found the quarrel between these two shocking. Probably, what set this particular case apart from the rest was the stature of the men involved, the significance of the work that was in contention, the length of time through which the controversy extended, and the sheer intensity of the dispute. Newton and Leibniz were at war in the later parts of their lives over a number of issues. Though the dispute was sparked off by the issue of priority over the invention of the calculus, the matter was made worse by the fact that they did not see eye to eye on the matter of the natural philosophy of the world. Newton’s action-at-a-distance theory of gravitation was viewed as a reversion to the times of occultism by Leibniz and many other mechanical philosophers of this era. This intermingling of philosophical issues with the priority issues over the invention of the calculus worsened the nature of the dispute. One of the reasons why the dispute assumed such alarming proportions and why both Newton and Leibniz were anxious to be considered the inventors of the calculus was because of the prevailing 17th century conventions about priority and attitude towards plagiarism. -
Calculus Terminology
AP Calculus BC Calculus Terminology Absolute Convergence Asymptote Continued Sum Absolute Maximum Average Rate of Change Continuous Function Absolute Minimum Average Value of a Function Continuously Differentiable Function Absolutely Convergent Axis of Rotation Converge Acceleration Boundary Value Problem Converge Absolutely Alternating Series Bounded Function Converge Conditionally Alternating Series Remainder Bounded Sequence Convergence Tests Alternating Series Test Bounds of Integration Convergent Sequence Analytic Methods Calculus Convergent Series Annulus Cartesian Form Critical Number Antiderivative of a Function Cavalieri’s Principle Critical Point Approximation by Differentials Center of Mass Formula Critical Value Arc Length of a Curve Centroid Curly d Area below a Curve Chain Rule Curve Area between Curves Comparison Test Curve Sketching Area of an Ellipse Concave Cusp Area of a Parabolic Segment Concave Down Cylindrical Shell Method Area under a Curve Concave Up Decreasing Function Area Using Parametric Equations Conditional Convergence Definite Integral Area Using Polar Coordinates Constant Term Definite Integral Rules Degenerate Divergent Series Function Operations Del Operator e Fundamental Theorem of Calculus Deleted Neighborhood Ellipsoid GLB Derivative End Behavior Global Maximum Derivative of a Power Series Essential Discontinuity Global Minimum Derivative Rules Explicit Differentiation Golden Spiral Difference Quotient Explicit Function Graphic Methods Differentiable Exponential Decay Greatest Lower Bound Differential -
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. -
Newton: 'Opticks'
University of Dayton eCommons Imprints and Impressions: Milestones in Human Commentaries on the Exhibit’s Works Progress November 2014 Newton: ‘Opticks’ Follow this and additional works at: http://ecommons.udayton.edu/rosebk_commentary Recommended Citation "Newton: ‘Opticks’" (2014). Commentaries on the Exhibit’s Works. 30. http://ecommons.udayton.edu/rosebk_commentary/30 This Article is brought to you for free and open access by the Imprints and Impressions: Milestones in Human Progress at eCommons. It has been accepted for inclusion in Commentaries on the Exhibit’s Works by an authorized administrator of eCommons. For more information, please contact [email protected], [email protected]. Reflections on the various works in the exhibit Imprints and Impressions: Milestones in Human Progress Highlights from the Rose Rare Book Collection, Sept. 29-Nov. 9, 2014 Roesch Library, University of Dayton Isaac Newton Opticks: Or, a Treatise of the Reflexions, Refractions, Inflexions and Colours of Light London, 1704 First edition Reflection 1 Perhaps the key point of Newton’s observations was that white light is made up ow is a rainbow formed? Why of a superposition of many different “rays” do I see colors like magenta, of light with different colors. Furthermore, H yellow, and turquoise blue on an each individual color of light, in a oily puddle or when my kids blow soap continuous spectrum, has its own property bubbles? Why does a prism project the of “refrangibility,” or refraction. When a ray colors of the rainbow on the wall? And why of red light (such as a laser pointer) hits a are all the really big telescopes based on glass surface at an angle, it will be bent as it mirrors and not lenses, which was the passes into the new medium. -
Infinitesimal Calculus
Infinitesimal Calculus Δy ΔxΔy and “cannot stand” Δx • Derivative of the sum/difference of two functions (x + Δx) ± (y + Δy) = (x + y) + Δx + Δy ∴ we have a change of Δx + Δy. • Derivative of the product of two functions (x + Δx)(y + Δy) = xy + Δxy + xΔy + ΔxΔy ∴ we have a change of Δxy + xΔy. • Derivative of the product of three functions (x + Δx)(y + Δy)(z + Δz) = xyz + Δxyz + xΔyz + xyΔz + xΔyΔz + ΔxΔyz + xΔyΔz + ΔxΔyΔ ∴ we have a change of Δxyz + xΔyz + xyΔz. • Derivative of the quotient of three functions x Let u = . Then by the product rule above, yu = x yields y uΔy + yΔu = Δx. Substituting for u its value, we have xΔy Δxy − xΔy + yΔu = Δx. Finding the value of Δu , we have y y2 • Derivative of a power function (and the “chain rule”) Let y = x m . ∴ y = x ⋅ x ⋅ x ⋅...⋅ x (m times). By a generalization of the product rule, Δy = (xm−1Δx)(x m−1Δx)(x m−1Δx)...⋅ (xm −1Δx) m times. ∴ we have Δy = mx m−1Δx. • Derivative of the logarithmic function Let y = xn , n being constant. Then log y = nlog x. Differentiating y = xn , we have dy dy dy y y dy = nxn−1dx, or n = = = , since xn−1 = . Again, whatever n−1 y dx x dx dx x x x the differentials of log x and log y are, we have d(log y) = n ⋅ d(log x), or d(log y) n = . Placing these values of n equal to each other, we obtain d(log x) dy d(log y) y dy = . -
Calculus Formulas and Theorems
Formulas and Theorems for Reference I. Tbigonometric Formulas l. sin2d+c,cis2d:1 sec2d l*cot20:<:sc:20 +.I sin(-d) : -sitt0 t,rs(-//) = t r1sl/ : -tallH 7. sin(A* B) :sitrAcosB*silBcosA 8. : siri A cos B - siu B <:os,;l 9. cos(A+ B) - cos,4cos B - siuA siriB 10. cos(A- B) : cosA cosB + silrA sirrB 11. 2 sirrd t:osd 12. <'os20- coS2(i - siu20 : 2<'os2o - I - 1 - 2sin20 I 13. tan d : <.rft0 (:ost/ I 14. <:ol0 : sirrd tattH 1 15. (:OS I/ 1 16. cscd - ri" 6i /F tl r(. cos[I ^ -el : sitt d \l 18. -01 : COSA 215 216 Formulas and Theorems II. Differentiation Formulas !(r") - trr:"-1 Q,:I' ]tra-fg'+gf' gJ'-,f g' - * (i) ,l' ,I - (tt(.r))9'(.,') ,i;.[tyt.rt) l'' d, \ (sttt rrJ .* ('oqI' .7, tJ, \ . ./ stll lr dr. l('os J { 1a,,,t,:r) - .,' o.t "11'2 1(<,ot.r') - (,.(,2.r' Q:T rl , (sc'c:.r'J: sPl'.r tall 11 ,7, d, - (<:s<t.r,; - (ls(].]'(rot;.r fr("'),t -.'' ,1 - fr(u") o,'ltrc ,l ,, 1 ' tlll ri - (l.t' .f d,^ --: I -iAl'CSllLl'l t!.r' J1 - rz 1(Arcsi' r) : oT Il12 Formulas and Theorems 2I7 III. Integration Formulas 1. ,f "or:artC 2. [\0,-trrlrl *(' .t "r 3. [,' ,t.,: r^x| (' ,I 4. In' a,,: lL , ,' .l 111Q 5. In., a.r: .rhr.r' .r r (' ,l f 6. sirr.r d.r' - ( os.r'-t C ./ 7. /.,,.r' dr : sitr.i'| (' .t 8. tl:r:hr sec,rl+ C or ln Jccrsrl+ C ,f'r^rr f 9.