The Limits of Predictability: Indeterminism and Undecidability - in Classical and Quantum Physics
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From Deterministic Dynamics to Thermodynamic Laws II: Fourier's
FROM DETERMINISTIC DYNAMICS TO THERMODYNAMIC LAWS II: FOURIER'S LAW AND MESOSCOPIC LIMIT EQUATION YAO LI Abstract. This paper considers the mesoscopic limit of a stochastic energy ex- change model that is numerically derived from deterministic dynamics. The law of large numbers and the central limit theorems are proved. We show that the limit of the stochastic energy exchange model is a discrete heat equation that satisfies Fourier's law. In addition, when the system size (number of particles) is large, the stochastic energy exchange is approximated by a stochastic differential equation, called the mesoscopic limit equation. 1. Introduction Fourier's law is an empirical law describing the relationship between the thermal conductivity and the temperature profile. In 1822, Fourier concluded that \the heat flux resulting from thermal conduction is proportional to the magnitude of the tem- perature gradient and opposite to it in sign" [14]. The well-known heat equation is derived based on Fourier's law. However, the rigorous derivation of Fourier's law from microscopic Hamiltonian mechanics remains to be a challenge to mathemati- cians and physicist [3]. This challenge mainly comes from our limited mathematical understanding to nonequilibrium statistical mechanics. After the foundations of sta- tistical mechanics were established by Boltzmann, Gibbs, and Maxwell more than a century ago, many things about nonequilibrium steady state (NESS) remains un- clear, especially the dependency of key quantities on the system size N. There have been several studies that aim to derive Fourier's law from the first principle. A large class of models [29, 31, 12, 11, 10] use anharmonic chains to de- scribe heat conduction in insulating crystals. -
Computational Complexity Theory Introduction to Computational
31/03/2012 Computational complexity theory Introduction to computational complexity theory Complexity (computability) theory theory deals with two aspects: Algorithm’s complexity. Problem’s complexity. References S. Cook, « The complexity of Theorem Proving Procedures », 1971. Garey and Johnson, « Computers and Intractability, A guide to the theory of NP-completeness », 1979. J. Carlier et Ph. Chrétienne « Problèmes d’ordonnancements : algorithmes et complexité », 1988. 1 31/03/2012 Basic Notions • Some problem is a “question” characterized by parameters and needs an answer. – Parameters description; – Properties that a solutions must satisfy; – An instance is obtained when the parameters are fixed to some values. • An algorithm: a set of instructions describing how some task can be achieved or a problem can be solved. • A program : the computational implementation of an algorithm. Algorithm’s complexity (I) • There may exists several algorithms for the same problem • Raised questions: – Which one to choose ? – How they are compared ? – How measuring the efficiency ? – What are the most appropriate measures, running time, memory space ? 2 31/03/2012 Algorithm’s complexity (II) • Running time depends on: – The data of the problem, – Quality of program..., – Computer type, – Algorithm’s efficiency, – etc. • Proceed by analyzing the algorithm: – Search for some n characterizing the data. – Compute the running time in terms of n. – Evaluating the number of elementary operations, (elementary operation = simple instruction of a programming language). Algorithm’s evaluation (I) • Any algorithm is composed of two main stages: initialization and computing one • The complexity parameter is the size data n (binary coding). Definition: Let be n>0 andT(n) the running time of an algorithm expressed in terms of the size data n, T(n) is of O(f(n)) iff n0 and some constant c such that: n n0, we have T(n) c f(n). -
Submitted Thesis
Saarland University Faculty of Mathematics and Computer Science Bachelor’s Thesis Synthetic One-One, Many-One, and Truth-Table Reducibility in Coq Author Advisor Felix Jahn Yannick Forster Reviewers Prof. Dr. Gert Smolka Prof. Dr. Markus Bläser Submitted: September 15th, 2020 ii Eidesstattliche Erklärung Ich erkläre hiermit an Eides statt, dass ich die vorliegende Arbeit selbstständig ver- fasst und keine anderen als die angegebenen Quellen und Hilfsmittel verwendet habe. Statement in Lieu of an Oath I hereby confirm that I have written this thesis on my own and that I have not used any other media or materials than the ones referred to in this thesis. Einverständniserklärung Ich bin damit einverstanden, dass meine (bestandene) Arbeit in beiden Versionen in die Bibliothek der Informatik aufgenommen und damit veröffentlicht wird. Declaration of Consent I agree to make both versions of my thesis (with a passing grade) accessible to the public by having them added to the library of the Computer Science Department. Saarbrücken, September 15th, 2020 Abstract Reducibility is an essential concept for undecidability proofs in computability the- ory. The idea behind reductions was conceived by Turing, who introduced the later so-called Turing reduction based on oracle machines. In 1944, Post furthermore in- troduced with one-one, many-one, and truth-table reductions in comparison to Tur- ing reductions more specific reducibility notions. Post then also started to analyze the structure of the different reducibility notions and their computability degrees. Most undecidable problems were reducible from the halting problem, since this was exactly the method to show them undecidable. However, Post was able to con- struct also semidecidable but undecidable sets that do not one-one, many-one, or truth-table reduce from the halting problem. -
Module 34: Reductions and NP-Complete Proofs
Module 34: Reductions and NP-Complete Proofs This module 34 focuses on reductions and proof of NP-Complete problems. The module illustrates the ways of reducing one problem to another. Then, the module illustrates the outlines of proof of NP- Complete problems with some examples. The objectives of this module are To understand the concept of Reductions among problems To Understand proof of NP-Complete problems To overview proof of some Important NP-Complete problems. Computational Complexity There are two types of complexity theories. One is related to algorithms, known as algorithmic complexity theory and another related to complexity of problems called computational complexity theory [2,3]. Algorithmic complexity theory [3,4] aims to analyze the algorithms in terms of the size of the problem. In modules 3 and 4, we had discussed about these methods. The size is the length of the input. It can be recollected from module 3 that the size of a number n is defined to be the number of binary bits needed to write n. For example, Example: b(5) = b(1012) = 3. In other words, the complexity of the algorithm is stated in terms of the size of the algorithm. Asymptotic Analysis [1,2] refers to the study of an algorithm as the input size reaches a limit and analyzing the behaviour of the algorithms. The asymptotic analysis is also science of approximation where the behaviour of the algorithm is expressed in terms of notations such as big-oh, Big-Omega and Big- Theta. Computational complexity theory is different. Computational complexity aims to determine complexity of the problems itself. -
Ergodic Theory Plays a Key Role in Multiple Fields Steven Ashley Science Writer
CORE CONCEPTS Core Concept: Ergodic theory plays a key role in multiple fields Steven Ashley Science Writer Statistical mechanics is a powerful set of professor Tom Ward, reached a key milestone mathematical tools that uses probability the- in the early 1930s when American mathema- ory to bridge the enormous gap between the tician George D. Birkhoff and Austrian-Hun- unknowable behaviors of individual atoms garian (and later, American) mathematician and molecules and those of large aggregate sys- and physicist John von Neumann separately tems of them—a volume of gas, for example. reconsidered and reformulated Boltzmann’ser- Fundamental to statistical mechanics is godic hypothesis, leading to the pointwise and ergodic theory, which offers a mathematical mean ergodic theories, respectively (see ref. 1). means to study the long-term average behavior These results consider a dynamical sys- of complex systems, such as the behavior of tem—whetheranidealgasorothercomplex molecules in a gas or the interactions of vi- systems—in which some transformation func- brating atoms in a crystal. The landmark con- tion maps the phase state of the system into cepts and methods of ergodic theory continue its state one unit of time later. “Given a mea- to play an important role in statistical mechan- sure-preserving system, a probability space ics, physics, mathematics, and other fields. that is acted on by the transformation in Ergodicity was first introduced by the a way that models physical conservation laws, Austrian physicist Ludwig Boltzmann laid Austrian physicist Ludwig Boltzmann in the what properties might it have?” asks Ward, 1870s, following on the originator of statisti- who is managing editor of the journal Ergodic the foundation for modern-day ergodic the- cal mechanics, physicist James Clark Max- Theory and Dynamical Systems.Themeasure ory. -
Decidable. (Recall It’S Not “Regular”.)
15-251: Great Theoretical Ideas in Computer Science Fall 2016 Lecture 6 September 15, 2016 Turing & the Uncomputable Comparing the cardinality of sets 퐴 ≤ 퐵 if there is an injection (one-to-one map) from 퐴 to 퐵 퐴 ≥ 퐵 if there is a surjection (onto map) from 퐴 to 퐵 퐴 = 퐵 if there is a bijection from 퐴 to 퐵 퐴 > |퐵| if there is no surjection from 퐵 to 퐴 (or equivalently, there is no injection from 퐴 to 퐵) Countable and uncountable sets countable countably infinite uncountable One slide guide to countability questions You are given a set 퐴 : is it countable or uncountable 퐴 ≤ |ℕ| or 퐴 > |ℕ| 퐴 ≤ |ℕ| : • Show directly surjection from ℕ to 퐴 • Show that 퐴 ≤ |퐵| where 퐵 ∈ {ℤ, ℤ x ℤ, ℚ, Σ∗, ℚ[x], …} 퐴 > |ℕ| : • Show directly using a diagonalization argument • Show that 퐴 ≥ | 0,1 ∞| Proving sets countable using computation For example, f(n) = ‘the nth prime’. You could write a program (Turing machine) to compute f. So this is a well-defined rule. Or: f(n) = the nth rational in our listing of ℚ. (List ℤ2 via the spiral, omit the terms p/0, omit rationals seen before…) You could write a program to compute this f. Poll Let 퐴 be the set of all languages over Σ = 1 ∗ Select the correct ones: - A is finite - A is infinite - A is countable - A is uncountable Another thing to remember from last week Encoding different objects with strings Fix some alphabet Σ . We use the ⋅ notation to denote the encoding of an object as a string in Σ∗ Examples: is the encoding a TM 푀 is the encoding a DFA 퐷 is the encoding of a pair of TMs 푀1, 푀2 is the encoding a pair 푀, 푥, where 푀 is a TM, and 푥 ∈ Σ∗ is an input to 푀 Uncountable to uncomputable The real number 1/7 is “computable”. -
AN INTRODUCTION to DYNAMICAL BILLIARDS Contents 1
AN INTRODUCTION TO DYNAMICAL BILLIARDS SUN WOO PARK 2 Abstract. Some billiard tables in R contain crucial references to dynamical systems but can be analyzed with Euclidean geometry. In this expository paper, we will analyze billiard trajectories in circles, circular rings, and ellipses as well as relate their charactersitics to ergodic theory and dynamical systems. Contents 1. Background 1 1.1. Recurrence 1 1.2. Invariance and Ergodicity 2 1.3. Rotation 3 2. Dynamical Billiards 4 2.1. Circle 5 2.2. Circular Ring 7 2.3. Ellipse 9 2.4. Completely Integrable 14 Acknowledgments 15 References 15 Dynamical billiards exhibits crucial characteristics related to dynamical systems. Some billiard tables in R2 can be understood with Euclidean geometry. In this ex- pository paper, we will analyze some of the billiard tables in R2, specifically circles, circular rings, and ellipses. In the first section we will present some preliminary background. In the second section we will analyze billiard trajectories in the afore- mentioned billiard tables and relate their characteristics with dynamical systems. We will also briefly discuss the notion of completely integrable billiard mappings and Birkhoff's conjecture. 1. Background (This section follows Chapter 1 and 2 of Chernov [1] and Chapter 3 and 4 of Rudin [2]) In this section, we define basic concepts in measure theory and ergodic theory. We will focus on probability measures, related theorems, and recurrent sets on certain maps. The definitions of probability measures and σ-algebra are in Chapter 1 of Chernov [1]. 1.1. Recurrence. Definition 1.1. Let (X,A,µ) and (Y ,B,υ) be measure spaces. -
Propagation of Rays in 2D and 3D Waveguides: a Stability Analysis with Lyapunov and Reversibility Fast Indicators G
Propagation of rays in 2D and 3D waveguides: a stability analysis with Lyapunov and Reversibility fast indicators G. Gradoni,1, a) F. Panichi,2, b) and G. Turchetti3, c) 1)School of Mathematical Sciences and Department of Electrical and Electronic Engineering, University of Nottingham, United Kingdomd) 2)Department of Physics and Astronomy , University of Bologna, Italy 3)Department of Physics and Astronomy, University of Bologna, Italy. INDAM National Group of Mathematical Physics, Italy (Dated: 13 January 2021) Propagation of rays in 2D and 3D corrugated waveguides is performed in the general framework of stability indicators. The analysis of stability is based on the Lyapunov and Reversibility error. It is found that the error growth follows a power law for regular orbits and an exponential law for chaotic orbits. A relation with the Shannon channel capacity is devised and an approximate scaling law found for the capacity increase with the corrugation depth. We investigate the propagation of a ray in a 2D wave guide whose boundaries are two parallel horizontal lines, with a periodic corrugation on the upper line. The reflection point abscissa on the lower line and the ray horizontal I. INTRODUCTION velocity component after reflection are the phase space coordinates and the map connecting two consecutive re- The equivalence between geometrical optics and mechanics flections is symplectic. The dynamic behaviour is illus- was established in a variational form by the principles of Fer- trated by the phase portraits which show that the regions mat and Maupertuis. If a ray propagates in a uniform medium of chaotic motion increase with the corrugation amplitude. -
Computability on the Integers
Computability on the integers Laurent Bienvenu ( LIAFA, CNRS & Université de Paris 7 ) EJCIM 2011 Amiens, France 30 mars 2011 1. Basic objects of computability The formalization and study of the notion of computable function is what computability theory is about. As opposed to complexity theory, we do not care about efficiency, just about feasibility. Computable. functions What does it means for a function f : N ! N to be computable? 1. Basic objects of computability 3/79 As opposed to complexity theory, we do not care about efficiency, just about feasibility. Computable. functions What does it means for a function f : N ! N to be computable? The formalization and study of the notion of computable function is what computability theory is about. 1. Basic objects of computability 3/79 Computable. functions What does it means for a function f : N ! N to be computable? The formalization and study of the notion of computable function is what computability theory is about. As opposed to complexity theory, we do not care about efficiency, just about feasibility. 1. Basic objects of computability 3/79 But for us, it is now obvious that computable = realizable by a program / algorithm. Surprisingly, the first acceptable formalization (Turing machines) is still one of the best (if not the best) we know today. The. intuition At the time these questions were first considered (1930’s), computers did not exist (at least in the modern sense). 1. Basic objects of computability 4/79 Surprisingly, the first acceptable formalization (Turing machines) is still one of the best (if not the best) we know today. -
Computability Theory
CSC 438F/2404F Notes (S. Cook and T. Pitassi) Fall, 2019 Computability Theory This section is partly inspired by the material in \A Course in Mathematical Logic" by Bell and Machover, Chap 6, sections 1-10. Other references: \Introduction to the theory of computation" by Michael Sipser, and \Com- putability, Complexity, and Languages" by M. Davis and E. Weyuker. Our first goal is to give a formal definition for what it means for a function on N to be com- putable by an algorithm. Historically the first convincing such definition was given by Alan Turing in 1936, in his paper which introduced what we now call Turing machines. Slightly before Turing, Alonzo Church gave a definition based on his lambda calculus. About the same time G¨odel,Herbrand, and Kleene developed definitions based on recursion schemes. Fortunately all of these definitions are equivalent, and each of many other definitions pro- posed later are also equivalent to Turing's definition. This has lead to the general belief that these definitions have got it right, and this assertion is roughly what we now call \Church's Thesis". A natural definition of computable function f on N allows for the possibility that f(x) may not be defined for all x 2 N, because algorithms do not always halt. Thus we will use the symbol 1 to mean “undefined". Definition: A partial function is a function n f :(N [ f1g) ! N [ f1g; n ≥ 0 such that f(c1; :::; cn) = 1 if some ci = 1. In the context of computability theory, whenever we refer to a function on N, we mean a partial function in the above sense. -
© 2020 Alexandra Q. Nilles DESIGNING BOUNDARY INTERACTIONS for SIMPLE MOBILE ROBOTS
© 2020 Alexandra Q. Nilles DESIGNING BOUNDARY INTERACTIONS FOR SIMPLE MOBILE ROBOTS BY ALEXANDRA Q. NILLES DISSERTATION Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Computer Science in the Graduate College of the University of Illinois at Urbana-Champaign, 2020 Urbana, Illinois Doctoral Committee: Professor Steven M. LaValle, Chair Professor Nancy M. Amato Professor Sayan Mitra Professor Todd D. Murphey, Northwestern University Abstract Mobile robots are becoming increasingly common for applications such as logistics and delivery. While most research for mobile robots focuses on generating collision-free paths, however, an environment may be so crowded with obstacles that allowing contact with environment boundaries makes our robot more efficient or our plans more robust. The robot may be so small or in a remote environment such that traditional sensing and communication is impossible, and contact with boundaries can help reduce uncertainty in the robot's state while navigating. These novel scenarios call for novel system designs, and novel system design tools. To address this gap, this thesis presents a general approach to modelling and planning over interactions between a robot and boundaries of its environment, and presents prototypes or simulations of such systems for solving high-level tasks such as object manipulation. One major contribution of this thesis is the derivation of necessary and sufficient conditions of stable, periodic trajectories for \bouncing robots," a particular model of point robots that move in straight lines between boundary interactions. Another major contribution is the description and implementation of an exact geometric planner for bouncing robots. We demonstrate the planner on traditional trajectory generation from start to goal states, as well as how to specify and generate stable periodic trajectories. -
(2020) Physics-Enhanced Neural Networks Learn Order and Chaos
PHYSICAL REVIEW E 101, 062207 (2020) Physics-enhanced neural networks learn order and chaos Anshul Choudhary ,1 John F. Lindner ,1,2,* Elliott G. Holliday,1 Scott T. Miller ,1 Sudeshna Sinha,1,3 and William L. Ditto 1 1Nonlinear Artificial Intelligence Laboratory, Physics Department, North Carolina State University, Raleigh, North Carolina 27607, USA 2Physics Department, The College of Wooster, Wooster, Ohio 44691, USA 3Indian Institute of Science Education and Research Mohali, Knowledge City, SAS Nagar, Sector 81, Manauli PO 140 306, Punjab, India (Received 26 November 2019; revised manuscript received 22 May 2020; accepted 24 May 2020; published 18 June 2020) Artificial neural networks are universal function approximators. They can forecast dynamics, but they may need impractically many neurons to do so, especially if the dynamics is chaotic. We use neural networks that incorporate Hamiltonian dynamics to efficiently learn phase space orbits even as nonlinear systems transition from order to chaos. We demonstrate Hamiltonian neural networks on a widely used dynamics benchmark, the Hénon-Heiles potential, and on nonperturbative dynamical billiards. We introspect to elucidate the Hamiltonian neural network forecasting. DOI: 10.1103/PhysRevE.101.062207 I. INTRODUCTION dynamical systems. But from stormy weather to swirling galaxies, natural dynamics is far richer and more challeng- Newton wrote, “My brain never hurt more than in my ing. In this article, we exploit the Hamiltonian structure of studies of the moon (and Earth and Sun)” [1]. Unsurprising conservative systems to provide neural networks with the sentiment, as the seemingly simple three-body problem is in- physics intelligence needed to learn the mix of order and trinsically intractable and practically unpredictable.