Hamiltonian Mechanics
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Relativistic Dynamics
Chapter 4 Relativistic dynamics We have seen in the previous lectures that our relativity postulates suggest that the most efficient (lazy but smart) approach to relativistic physics is in terms of 4-vectors, and that velocities never exceed c in magnitude. In this chapter we will see how this 4-vector approach works for dynamics, i.e., for the interplay between motion and forces. A particle subject to forces will undergo non-inertial motion. According to Newton, there is a simple (3-vector) relation between force and acceleration, f~ = m~a; (4.0.1) where acceleration is the second time derivative of position, d~v d2~x ~a = = : (4.0.2) dt dt2 There is just one problem with these relations | they are wrong! Newtonian dynamics is a good approximation when velocities are very small compared to c, but outside of this regime the relation (4.0.1) is simply incorrect. In particular, these relations are inconsistent with our relativity postu- lates. To see this, it is sufficient to note that Newton's equations (4.0.1) and (4.0.2) predict that a particle subject to a constant force (and initially at rest) will acquire a velocity which can become arbitrarily large, Z t ~ d~v 0 f ~v(t) = 0 dt = t ! 1 as t ! 1 . (4.0.3) 0 dt m This flatly contradicts the prediction of special relativity (and causality) that no signal can propagate faster than c. Our task is to understand how to formulate the dynamics of non-inertial particles in a manner which is consistent with our relativity postulates (and then verify that it matches observation, including in the non-relativistic regime). -
Computational Thermodynamics: a Mature Scientific Tool for Industry and Academia*
Pure Appl. Chem., Vol. 83, No. 5, pp. 1031–1044, 2011. doi:10.1351/PAC-CON-10-12-06 © 2011 IUPAC, Publication date (Web): 4 April 2011 Computational thermodynamics: A mature scientific tool for industry and academia* Klaus Hack GTT Technologies, Kaiserstrasse 100, D-52134 Herzogenrath, Germany Abstract: The paper gives an overview of the general theoretical background of computa- tional thermochemistry as well as recent developments in the field, showing special applica- tion cases for real world problems. The established way of applying computational thermo- dynamics is the use of so-called integrated thermodynamic databank systems (ITDS). A short overview of the capabilities of such an ITDS is given using FactSage as an example. However, there are many more applications that go beyond the closed approach of an ITDS. With advanced algorithms it is possible to include explicit reaction kinetics as an additional constraint into the method of complex equilibrium calculations. Furthermore, a method of interlinking a small number of local equilibria with a system of materials and energy streams has been developed which permits a thermodynamically based approach to process modeling which has proven superior to detailed high-resolution computational fluid dynamic models in several cases. Examples for such highly developed applications of computational thermo- dynamics will be given. The production of metallurgical grade silicon from silica and carbon will be used to demonstrate the application of several calculation methods up to a full process model. Keywords: complex equilibria; Gibbs energy; phase diagrams; process modeling; reaction equilibria; thermodynamics. INTRODUCTION The concept of using Gibbsian thermodynamics as an approach to tackle problems of industrial or aca- demic background is not new at all. -
OCC D 5 Gen5d Eee 1305 1A E
this cover and their final version of the extended essay to is are not is chose to write about applications of differential calculus because she found a great interest in it during her IB Math class. She wishes she had time to complete a deeper analysis of her topic; however, her busy schedule made it difficult so she is somewhat disappointed with the outcome of her essay. It was a pleasure meeting with when she was able to and her understanding of her topic was evident during our viva voce. I, too, wish she had more time to complete a more thorough investigation. Overall, however, I believe she did well and am satisfied with her essay. must not use Examiner 1 Examiner 2 Examiner 3 A research 2 2 D B introduction 2 2 c 4 4 D 4 4 E reasoned 4 4 D F and evaluation 4 4 G use of 4 4 D H conclusion 2 2 formal 4 4 abstract 2 2 holistic 4 4 Mathematics Extended Essay An Investigation of the Various Practical Uses of Differential Calculus in Geometry, Biology, Economics, and Physics Candidate Number: 2031 Words 1 Abstract Calculus is a field of math dedicated to analyzing and interpreting behavioral changes in terms of a dependent variable in respect to changes in an independent variable. The versatility of differential calculus and the derivative function is discussed and highlighted in regards to its applications to various other fields such as geometry, biology, economics, and physics. First, a background on derivatives is provided in regards to their origin and evolution, especially as apparent in the transformation of their notations so as to include various individuals and ways of denoting derivative properties. -
Covariant Hamiltonian Field Theory 3
December 16, 2020 2:58 WSPC/INSTRUCTION FILE kfte COVARIANT HAMILTONIAN FIELD THEORY JURGEN¨ STRUCKMEIER and ANDREAS REDELBACH GSI Helmholtzzentrum f¨ur Schwerionenforschung GmbH Planckstr. 1, 64291 Darmstadt, Germany and Johann Wolfgang Goethe-Universit¨at Frankfurt am Main Max-von-Laue-Str. 1, 60438 Frankfurt am Main, Germany [email protected] Received 18 July 2007 Revised 14 December 2020 A consistent, local coordinate formulation of covariant Hamiltonian field theory is pre- sented. Whereas the covariant canonical field equations are equivalent to the Euler- Lagrange field equations, the covariant canonical transformation theory offers more gen- eral means for defining mappings that preserve the form of the field equations than the usual Lagrangian description. It is proved that Poisson brackets, Lagrange brackets, and canonical 2-forms exist that are invariant under canonical transformations of the fields. The technique to derive transformation rules for the fields from generating functions is demonstrated by means of various examples. In particular, it is shown that the infinites- imal canonical transformation furnishes the most general form of Noether’s theorem. We furthermore specify the generating function of an infinitesimal space-time step that conforms to the field equations. Keywords: Field theory; Hamiltonian density; covariant. PACS numbers: 11.10.Ef, 11.15Kc arXiv:0811.0508v6 [math-ph] 15 Dec 2020 1. Introduction Relativistic field theories and gauge theories are commonly formulated on the basis of a Lagrangian density L1,2,3,4. The space-time evolution of the fields is obtained by integrating the Euler-Lagrange field equations that follow from the four-dimensional representation of Hamilton’s action principle. -
Time-Derivative Models of Pavlovian Reinforcement Richard S
Approximately as appeared in: Learning and Computational Neuroscience: Foundations of Adaptive Networks, M. Gabriel and J. Moore, Eds., pp. 497–537. MIT Press, 1990. Chapter 12 Time-Derivative Models of Pavlovian Reinforcement Richard S. Sutton Andrew G. Barto This chapter presents a model of classical conditioning called the temporal- difference (TD) model. The TD model was originally developed as a neuron- like unit for use in adaptive networks (Sutton and Barto 1987; Sutton 1984; Barto, Sutton and Anderson 1983). In this paper, however, we analyze it from the point of view of animal learning theory. Our intended audience is both animal learning researchers interested in computational theories of behavior and machine learning researchers interested in how their learning algorithms relate to, and may be constrained by, animal learning studies. For an exposition of the TD model from an engineering point of view, see Chapter 13 of this volume. We focus on what we see as the primary theoretical contribution to animal learning theory of the TD and related models: the hypothesis that reinforcement in classical conditioning is the time derivative of a compos- ite association combining innate (US) and acquired (CS) associations. We call models based on some variant of this hypothesis time-derivative mod- els, examples of which are the models by Klopf (1988), Sutton and Barto (1981a), Moore et al (1986), Hawkins and Kandel (1984), Gelperin, Hop- field and Tank (1985), Tesauro (1987), and Kosko (1986); we examine several of these models in relation to the TD model. We also briefly ex- plore relationships with animal learning theories of reinforcement, including Mowrer’s drive-induction theory (Mowrer 1960) and the Rescorla-Wagner model (Rescorla and Wagner 1972). -
Branched Hamiltonians and Supersymmetry
Branched Hamiltonians and Supersymmetry Thomas Curtright, University of Miami Wigner 111 seminar, 12 November 2013 Some examples of branched Hamiltonians are explored, as recently advo- cated by Shapere and Wilczek. These are actually cases of switchback poten- tials, albeit in momentum space, as previously analyzed for quasi-Hamiltonian dynamical systems in a classical context. A basic model, with a pair of Hamiltonian branches related by supersymmetry, is considered as an inter- esting illustration, and as stimulation. “It is quite possible ... we may discover that in nature the relation of past and future is so intimate ... that no simple representation of a present may exist.” – R P Feynman Based on work with Cosmas Zachos, Argonne National Laboratory Introduction to the problem In quantum mechanics H = p2 + V (x) (1) is neither more nor less difficult than H = x2 + V (p) (2) by reason of x, p duality, i.e. the Fourier transform: ψ (x) φ (p) ⎫ ⎧ x ⎪ ⎪ +i∂/∂p ⎪ ⇐⇒ ⎪ ⎬⎪ ⎨⎪ i∂/∂x p − ⎪ ⎪ ⎪ ⎪ ⎭⎪ ⎩⎪ This equivalence of (1) and (2) is manifest in the QMPS formalism, as initiated by Wigner (1932), 1 2ipy/ f (x, p)= dy x + y ρ x y e− π | | − 1 = dk p + k ρ p k e2ixk/ π | | − where x and p are on an equal footing, and where even more general H (x, p) can be considered. See CZ to follow, and other talks at this conference. Or even better, in addition to the excellent books cited at the conclusion of Professor Schleich’s talk yesterday morning, please see our new book on the subject ... Even in classical Hamiltonian mechanics, (1) and (2) are equivalent under a classical canonical transformation on phase space: (x, p) (p, x) ⇐⇒ − But upon transitioning to Lagrangian mechanics, the equivalence between the two theories becomes obscure. -
1 the Basic Set-Up 2 Poisson Brackets
MATHEMATICS 7302 (Analytical Dynamics) YEAR 2016–2017, TERM 2 HANDOUT #12: THE HAMILTONIAN APPROACH TO MECHANICS These notes are intended to be read as a supplement to the handout from Gregory, Classical Mechanics, Chapter 14. 1 The basic set-up I assume that you have already studied Gregory, Sections 14.1–14.4. The following is intended only as a succinct summary. We are considering a system whose equations of motion are written in Hamiltonian form. This means that: 1. The phase space of the system is parametrized by canonical coordinates q =(q1,...,qn) and p =(p1,...,pn). 2. We are given a Hamiltonian function H(q, p, t). 3. The dynamics of the system is given by Hamilton’s equations of motion ∂H q˙i = (1a) ∂pi ∂H p˙i = − (1b) ∂qi for i =1,...,n. In these notes we will consider some deeper aspects of Hamiltonian dynamics. 2 Poisson brackets Let us start by considering an arbitrary function f(q, p, t). Then its time evolution is given by n df ∂f ∂f ∂f = q˙ + p˙ + (2a) dt ∂q i ∂p i ∂t i=1 i i X n ∂f ∂H ∂f ∂H ∂f = − + (2b) ∂q ∂p ∂p ∂q ∂t i=1 i i i i X 1 where the first equality used the definition of total time derivative together with the chain rule, and the second equality used Hamilton’s equations of motion. The formula (2b) suggests that we make a more general definition. Let f(q, p, t) and g(q, p, t) be any two functions; we then define their Poisson bracket {f,g} to be n def ∂f ∂g ∂f ∂g {f,g} = − . -
Hamilton's Equations. Conservation Laws. Reduction. Poisson Brackets
Hamiltonian Formalism: Hamilton's equations. Conservation laws. Reduction. Poisson Brackets. Physics 6010, Fall 2016 Hamiltonian Formalism: Hamilton's equations. Conservation laws. Reduction. Poisson Brackets. Relevant Sections in Text: 8.1 { 8.3, 9.5 The Hamiltonian Formalism We now return to formal developments: a study of the Hamiltonian formulation of mechanics. This formulation of mechanics is in many ways more powerful than the La- grangian formulation. Among the advantages of Hamiltonian mechanics we note that: it leads to powerful geometric techniques for studying the properties of dynamical systems, it allows for a beautiful expression of the relation between symmetries and conservation laws, and it leads to many structures that can be viewed as the macroscopic (\classical") imprint of quantum mechanics. Although the Hamiltonian form of mechanics is logically independent of the Lagrangian formulation, it is convenient and instructive to introduce the Hamiltonian formalism via transition from the Lagrangian formalism, since we have already developed the latter. (Later I will indicate how to give an ab initio development of the Hamiltonian formal- ism.) The most basic change we encounter when passing from Lagrangian to Hamiltonian methods is that the \arena" we use to describe the equations of motion is no longer the configuration space, or even the velocity phase space, but rather the momentum phase space. Recall that the Lagrangian formalism is defined once one specifies a configuration space Q (coordinates qi) and then the velocity phase space Ω (coordinates (qi; q_i)). The mechanical system is defined by a choice of Lagrangian, L, which is a function on Ω (and possible the time): L = L(qi; q_i; t): Curves in the configuration space Q { or in the velocity phase space Ω { satisfying the Euler-Lagrange (EL) equations, @L d @L − = 0; @qi dt @q_i define the dynamical behavior of the system. -
Poisson Structures and Integrability
Poisson Structures and Integrability Peter J. Olver University of Minnesota http://www.math.umn.edu/ olver ∼ Hamiltonian Systems M — phase space; dim M = 2n Local coordinates: z = (p, q) = (p1, . , pn, q1, . , qn) Canonical Hamiltonian system: dz O I = J H J = − dt ∇ ! I O " Equivalently: dpi ∂H dqi ∂H = = dt − ∂qi dt ∂pi Lagrange Bracket (1808): n ∂pi ∂qi ∂qi ∂pi [ u , v ] = ∂u ∂v − ∂u ∂v i#= 1 (Canonical) Poisson Bracket (1809): n ∂u ∂v ∂u ∂v u , v = { } ∂pi ∂qi − ∂qi ∂pi i#= 1 Given functions u , . , u , the (2n) (2n) matrices with 1 2n × respective entries [ u , u ] u , u i, j = 1, . , 2n i j { i j } are mutually inverse. Canonical Poisson Bracket n ∂F ∂H ∂F ∂H F, H = F T J H = { } ∇ ∇ ∂pi ∂qi − ∂qi ∂pi i#= 1 = Poisson (1809) ⇒ Hamiltonian flow: dz = z, H = J H dt { } ∇ = Hamilton (1834) ⇒ First integral: dF F, H = 0 = 0 F (z(t)) = const. { } ⇐⇒ dt ⇐⇒ Poisson Brackets , : C∞(M, R) C∞(M, R) C∞(M, R) { · · } × −→ Bilinear: a F + b G, H = a F, H + b G, H { } { } { } F, a G + b H = a F, G + b F, H { } { } { } Skew Symmetric: F, H = H, F { } − { } Jacobi Identity: F, G, H + H, F, G + G, H, F = 0 { { } } { { } } { { } } Derivation: F, G H = F, G H + G F, H { } { } { } F, G, H C∞(M, R), a, b R. ∈ ∈ In coordinates z = (z1, . , zm), F, H = F T J(z) H { } ∇ ∇ where J(z)T = J(z) is a skew symmetric matrix. − The Jacobi identity imposes a system of quadratically nonlinear partial differential equations on its entries: ∂J jk ∂J ki ∂J ij J il + J jl + J kl = 0 ! ∂zl ∂zl ∂zl " #l Given a Poisson structure, the Hamiltonian flow corresponding to H C∞(M, R) is the system of ordinary differential equati∈ons dz = z, H = J(z) H dt { } ∇ Lie’s Theory of Function Groups Used for integration of partial differential equations: F , F = G (F , . -
Time-Dependent Hamiltonian Mechanics on a Locally Conformal
Time-dependent Hamiltonian mechanics on a locally conformal symplectic manifold Orlando Ragnisco†, Cristina Sardón∗, Marcin Zając∗∗ Department of Mathematics and Physics†, Universita degli studi Roma Tre, Largo S. Leonardo Murialdo, 1, 00146 , Rome, Italy. ragnisco@fis.uniroma3.it Department of Applied Mathematics∗, Universidad Polit´ecnica de Madrid. C/ Jos´eGuti´errez Abascal, 2, 28006, Madrid. Spain. [email protected] Department of Mathematical Methods in Physics∗∗, Faculty of Physics. University of Warsaw, ul. Pasteura 5, 02-093 Warsaw, Poland. [email protected] Abstract In this paper we aim at presenting a concise but also comprehensive study of time-dependent (t- dependent) Hamiltonian dynamics on a locally conformal symplectic (lcs) manifold. We present a generalized geometric theory of canonical transformations and formulate a time-dependent geometric Hamilton-Jacobi theory on lcs manifolds. In contrast to previous papers concerning locally conformal symplectic manifolds, here the introduction of the time dependency brings out interesting geometric properties, as it is the introduction of contact geometry in locally symplectic patches. To conclude, we show examples of the applications of our formalism, in particular, we present systems of differential equations with time-dependent parameters, which admit different physical interpretations as we shall point out. arXiv:2104.02636v1 [math-ph] 6 Apr 2021 Contents 1 Introduction 2 2 Fundamentals on time-dependent Hamiltonian systems 5 2.1 Time-dependentsystems. ....... 5 2.2 Canonicaltransformations . ......... 6 2.3 Generating functions of canonical transformations . ................ 8 1 3 Geometry of locally conformal symplectic manifolds 8 3.1 Basics on locally conformal symplectic manifolds . ............... 8 3.2 Locally conformal symplectic structures on cotangent bundles............ -
Thermodynamics
ME346A Introduction to Statistical Mechanics { Wei Cai { Stanford University { Win 2011 Handout 6. Thermodynamics January 26, 2011 Contents 1 Laws of thermodynamics 2 1.1 The zeroth law . .3 1.2 The first law . .4 1.3 The second law . .5 1.3.1 Efficiency of Carnot engine . .5 1.3.2 Alternative statements of the second law . .7 1.4 The third law . .8 2 Mathematics of thermodynamics 9 2.1 Equation of state . .9 2.2 Gibbs-Duhem relation . 11 2.2.1 Homogeneous function . 11 2.2.2 Virial theorem / Euler theorem . 12 2.3 Maxwell relations . 13 2.4 Legendre transform . 15 2.5 Thermodynamic potentials . 16 3 Worked examples 21 3.1 Thermodynamic potentials and Maxwell's relation . 21 3.2 Properties of ideal gas . 24 3.3 Gas expansion . 28 4 Irreversible processes 32 4.1 Entropy and irreversibility . 32 4.2 Variational statement of second law . 32 1 In the 1st lecture, we will discuss the concepts of thermodynamics, namely its 4 laws. The most important concepts are the second law and the notion of Entropy. (reading assignment: Reif x 3.10, 3.11) In the 2nd lecture, We will discuss the mathematics of thermodynamics, i.e. the machinery to make quantitative predictions. We will deal with partial derivatives and Legendre transforms. (reading assignment: Reif x 4.1-4.7, 5.1-5.12) 1 Laws of thermodynamics Thermodynamics is a branch of science connected with the nature of heat and its conver- sion to mechanical, electrical and chemical energy. (The Webster pocket dictionary defines, Thermodynamics: physics of heat.) Historically, it grew out of efforts to construct more efficient heat engines | devices for ex- tracting useful work from expanding hot gases (http://www.answers.com/thermodynamics). -
Canonical Transformations (Lecture 4)
Canonical transformations (Lecture 4) January 26, 2016 61/441 Lecture outline We will introduce and discuss canonical transformations that conserve the Hamiltonian structure of equations of motion. Poisson brackets are used to verify that a given transformation is canonical. A practical way to devise canonical transformation is based on usage of generation functions. The motivation behind this study is to understand the freedom which we have in the choice of various sets of coordinates and momenta. Later we will use this freedom to select a convenient set of coordinates for description of partilcle's motion in an accelerator. 62/441 Introduction Within the Lagrangian approach we can choose the generalized coordinates as we please. We can start with a set of coordinates qi and then introduce generalized momenta pi according to Eqs. @L(qk ; q_k ; t) pi = ; i = 1;:::; n ; @q_i and form the Hamiltonian ! H = pi q_i - L(qk ; q_k ; t) : i X Or, we can chose another set of generalized coordinates Qi = Qi (qk ; t), express the Lagrangian as a function of Qi , and obtain a different set of momenta Pi and a different Hamiltonian 0 H (Qi ; Pi ; t). This type of transformation is called a point transformation. The two representations are physically equivalent and they describe the same dynamics of our physical system. 63/441 Introduction A more general approach to the problem of using various variables in Hamiltonian formulation of equations of motion is the following. Let us assume that we have canonical variables qi , pi and the corresponding Hamiltonian H(qi ; pi ; t) and then make a transformation to new variables Qi = Qi (qk ; pk ; t) ; Pi = Pi (qk ; pk ; t) : i = 1 ::: n: (4.1) 0 Can we find a new Hamiltonian H (Qi ; Pi ; t) such that the system motion in new variables satisfies Hamiltonian equations with H 0? What are requirements on the transformation (4.1) for such a Hamiltonian to exist? These questions lead us to canonical transformations.