THE BEGINNING of the MONTE CARLO METHOD by N
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The Development of Military Nuclear Strategy And
The Development of Military Nuclear Strategy and Anglo-American Relations, 1939 – 1958 Submitted by: Geoffrey Charles Mallett Skinner to the University of Exeter as a thesis for the degree of Doctor of Philosophy in History, July 2018 This thesis is available for Library use on the understanding that it is copyright material and that no quotation from the thesis may be published without proper acknowledgement. I certify that all material in this thesis which is not my own work has been identified and that no material has previously been submitted and approved for the award of a degree by this or any other University. (Signature) ……………………………………………………………………………… 1 Abstract There was no special governmental partnership between Britain and America during the Second World War in atomic affairs. A recalibration is required that updates and amends the existing historiography in this respect. The wartime atomic relations of those countries were cooperative at the level of science and resources, but rarely that of the state. As soon as it became apparent that fission weaponry would be the main basis of future military power, America decided to gain exclusive control over the weapon. Britain could not replicate American resources and no assistance was offered to it by its conventional ally. America then created its own, closed, nuclear system and well before the 1946 Atomic Energy Act, the event which is typically seen by historians as the explanation of the fracturing of wartime atomic relations. Immediately after 1945 there was insufficient systemic force to create change in the consistent American policy of atomic monopoly. As fusion bombs introduced a new magnitude of risk, and as the nuclear world expanded and deepened, the systemic pressures grew. -
Computer Simulations and the Trading Zone
PETER &A Computer Simulations and the Trading Zone At the root of most accounts of the development of science is the covert premise that science is about ontology: What objects are there? How do they interact? And how do we discover them? It is a premise that underlies three distinct epochs of inquiry into the nature of science. Among the positivists, the later Carnap explicitly advocated a view that the objects of physics would be irretrievably bound up with what he called a "framework," a full set of linguistic relations such as is found in Newtonian or Eiustcini;~mechanics. That these frameworks held little in common did not trouble Car- nap; prediction mattered more than progress. Kuhn molded this notion and gave it a more historical focus. Unlike the positivists, Kuhn and other commentators of the 1960's wanted to track the process by which a community abandoned one framework and adopted another. To more recent scholars, the Kuhnian categoriza- tion of group affiliation and disaffiliation was by and large correct, but its underlying dynamics were deemed deficient because they paid insufficient attention to the sociological forces that bound groups to their paradigms. All three generations embody the root assumption that the analysis of science studies (or ought to study) a science classified and divided according to the objects of its inquiry. All three assume that as these objects change, particular branches of science split into myriad disconnected parts. It is a view of scientific disunity that I will refer to as "framework relativism. 55 In this essay, as before, I will oppose this view, but not by Computer Simulations I 19 invoking the old positivist pipe dreams: no universal protocol languages, no physicalism, no Corntian hierarchy of knowledge, and no radical reductionism. -
The Convergence of Markov Chain Monte Carlo Methods 3
The Convergence of Markov chain Monte Carlo Methods: From the Metropolis method to Hamiltonian Monte Carlo Michael Betancourt From its inception in the 1950s to the modern frontiers of applied statistics, Markov chain Monte Carlo has been one of the most ubiquitous and successful methods in statistical computing. The development of the method in that time has been fueled by not only increasingly difficult problems but also novel techniques adopted from physics. In this article I will review the history of Markov chain Monte Carlo from its inception with the Metropolis method to the contemporary state-of-the-art in Hamiltonian Monte Carlo. Along the way I will focus on the evolving interplay between the statistical and physical perspectives of the method. This particular conceptual emphasis, not to mention the brevity of the article, requires a necessarily incomplete treatment. A complementary, and entertaining, discussion of the method from the statistical perspective is given in Robert and Casella (2011). Similarly, a more thorough but still very readable review of the mathematics behind Markov chain Monte Carlo and its implementations is given in the excellent survey by Neal (1993). I will begin with a discussion of the mathematical relationship between physical and statistical computation before reviewing the historical introduction of Markov chain Monte Carlo and its first implementations. Then I will continue to the subsequent evolution of the method with increasing more sophisticated implementations, ultimately leading to the advent of Hamiltonian Monte Carlo. 1. FROM PHYSICS TO STATISTICS AND BACK AGAIN At the dawn of the twentieth-century, physics became increasingly focused on under- arXiv:1706.01520v2 [stat.ME] 10 Jan 2018 standing the equilibrium behavior of thermodynamic systems, especially ensembles of par- ticles. -
The Real Holocaust
THE REAL HOLOCAUST "Hitler will have no war, but he will be forced into it, not this year but later..." Emil Ludwig [Jew], Les Annales, June 1934 "We possess several hundred atomic warheads and rockets and can launch them at targets in all directions, perhaps even at Rome. Most European capitals are targets for our air force. Let me quote General Moshe Dayan: 'Israel must be like a mad dog, too dangerous to bother.' I consider it all hopeless at this point. We shall have to try to prevent things from coming to that, if at all possible. Our armed forces, however, are not the thirtieth strongest in the world, but rather the second or third. We have the capability to take the world down with us. And I can assure you that that will happen before Israel goes under." -- Martin van Creveld, Israeli professor of military history at the Hebrew University in Jerusalem in an interview in the Dutch weekly magazine: Elsevier, 2002, no. 17, p. 52-53. "We may never actually have to use this atomic weapon in military operations as the mere threat of its use will persuade any opponent to surrender to us." –Chaim Weizmann [Jew] "Jews Declared War on Germany. Even before the war started, the Jewish leaders on a worldwide basis had years before, declared that world Jewry was at war with Germany, and that they would utilize their immense financial, moral, and political powers to destroy Hitler and Nazi Germany. Principal among these was Chaim Weizmann, the Zionist leader, who so declared on September 5, 1939. -
Monte Carlo Simulation Techniques
Monte Carlo Simulation Techniques Ji Qiang Accelerator Modeling Program Accelerator Technology & Applied Physics Division Lawrence Berkeley National Laboratory CERN Accelerator School, Thessaloniki, Greece Nov. 13, 2018 Introduction: What is the Monte Carlo Method? • What is the Monte Carlo method? - Monte Carlo method is a (computational) method that relies on the use of random sampling and probability statistics to obtain numerical results for solving deterministic or probabilistic problems “…a method of solving various problems in computational mathematics by constructing for each problem a random process with parameters equal to the required quantities of that problem. The unknowns are determined approximately by carrying out observations on the random process and by computing its statistical characteristics which are approximately equal to the required parameters.” J. H. Halton, “A retrospective and prospective survey of the Monte Carlo Method,” SIAM Review, Vol. 12, No. 1 (1970). Introduction: What can the Monte Carlo Method Do? • Give an approximate solution to a problem that is too big, too hard, too irregular for deterministic mathematical approach • Two types of applications: a) The problems that are stochastic (probabilistic) by nature: - particle transport, - telephone and other communication systems, - population studies based on the statistics of survival and reproduction. b) The problems that are deterministic by nature: - the evaluation of integrals, - solving partial differential equations • It has been used in areas as diverse as physics, chemistry, material science, economics, flow of traffic and many others. Brief History of the Monte Carlo Method • 1772 Comte de Buffon - earliest documented use of random sampling to solve a mathematical problem (the probability of needle crossing parallel lines). -
Berni Alder (1925–2020)
Obituary Berni Alder (1925–2020) Theoretical physicist who pioneered the computer modelling of matter. erni Alder pioneered computer sim- undergo a transition from liquid to solid. Since LLNL ulation, in particular of the dynamics hard spheres do not have attractive interac- of atoms and molecules in condensed tions, freezing maximizes their entropy rather matter. To answer fundamental ques- than minimizing their energy; the regular tions, he encouraged the view that arrangement of spheres in a crystal allows more Bcomputer simulation was a new way of doing space for them to move than does a liquid. science, one that could connect theory with A second advance concerned how non- experiment. Alder’s vision transformed the equilibrium fluids approach equilibrium: field of statistical mechanics and many other Albert Einstein, for example, assumed that areas of applied science. fluctuations in their properties would quickly Alder, who died on 7 September aged 95, decay. In 1970, Alder and Wainwright dis- was born in Duisburg, Germany. In 1933, as covered that this intuitive assumption was the Nazis came to power, his family moved to incorrect. If a sphere is given an initial push, Zurich, Switzerland, and in 1941 to the United its average velocity is found to decay much States. After wartime service in the US Navy, more slowly. This caused a re-examination of Alder obtained undergraduate and master’s the microscopic basis for hydrodynamics. degrees in chemistry from the University of Alder extended the reach of simulation. California, Berkeley. While working for a PhD In molecular dynamics, the forces between at the California Institute of Technology in molecules arise from the electronic density; Pasadena, under the physical chemist John these forces can be described only by quan- Kirkwood, he began to use mechanical com- tum mechanics. -
Lecture #8: Monte Carlo Method
Lecture #8: Monte Carlo method ENGG304: Uncertainty, Reliability and Risk Edoardo Patelli Institute for Risk and Uncertainty E: [email protected] W: www.liv.ac.uk/risk-and-uncertainty T: +44 01517944079 A MEMBER OF THE RUSSELL GROUP Edoardo Patelli University of Liverpool 18 March 2019 1 / 81 Lecture Outline 1 Introduction 2 Monte Carlo method Random Number Generator Sampling Methods Buffon’s experiment Monte Carlo integration Probability of failure 3 Summary 4 Computer based class Some useful slides Assignments Edoardo Patelli University of Liverpool 18 March 2019 2 / 81 Introduction Programme ENGG304 1 28/01/2019 Introduction 2 08/02/2019 Human error 3 11/02/2019 Qualitative risk assessment: Safety analysis 4 18/02/2019 Qualitative risk assessment: Event Tree and Fault Tree 5 25/02/2019 Tutorial I 6 04/03/2019 Model of random phenomena 7 11/03/2019 Structural reliability 8 18/03/2019 Monte Carlo simulation I + Hands-on session 9 25/03/2019 Tutorial II 10 01/04/2019 Monte Carlo simulation II + Tutorial III (Hands-on session) Edoardo Patelli University of Liverpool 18 March 2019 3 / 81 Introduction Summary Lecture #7 Safety Margin Fundamental problem Performance function defined as “Capacity” - “Demand” Safety margin: M = C − D = g(x) 2 For normal and independent random variables: M ∼ N(µM ; σM ) q 2 2 µM = µC − µD σM = σC + σD Reliability index β β = µM /σM represents the number of standard deviations by which the mean value of the safety margin M exceeds zero Edoardo Patelli University of Liverpool 18 March 2019 4 / 81 Introduction -
The Los Alamos Thermonuclear Weapon Project, 1942-1952
Igniting The Light Elements: The Los Alamos Thermonuclear Weapon Project, 1942-1952 by Anne Fitzpatrick Dissertation submitted to the Faculty of Virginia Polytechnic Institute and State University in partial fulfillment of the requirements for the degree of DOCTOR OF PHILOSOPHY in SCIENCE AND TECHNOLOGY STUDIES Approved: Joseph C. Pitt, Chair Richard M. Burian Burton I. Kaufman Albert E. Moyer Richard Hirsh June 23, 1998 Blacksburg, Virginia Keywords: Nuclear Weapons, Computing, Physics, Los Alamos National Laboratory Igniting the Light Elements: The Los Alamos Thermonuclear Weapon Project, 1942-1952 by Anne Fitzpatrick Committee Chairman: Joseph C. Pitt Science and Technology Studies (ABSTRACT) The American system of nuclear weapons research and development was conceived and developed not as a result of technological determinism, but by a number of individual architects who promoted the growth of this large technologically-based complex. While some of the technological artifacts of this system, such as the fission weapons used in World War II, have been the subject of many historical studies, their technical successors -- fusion (or hydrogen) devices -- are representative of the largely unstudied highly secret realms of nuclear weapons science and engineering. In the postwar period a small number of Los Alamos Scientific Laboratory’s staff and affiliates were responsible for theoretical work on fusion weapons, yet the program was subject to both the provisions and constraints of the U. S. Atomic Energy Commission, of which Los Alamos was a part. The Commission leadership’s struggle to establish a mission for its network of laboratories, least of all to keep them operating, affected Los Alamos’s leaders’ decisions as to the course of weapons design and development projects. -
Oral History Interview with Nicolas C. Metropolis
An Interview with NICOLAS C. METROPOLIS OH 135 Conducted by William Aspray on 29 May 1987 Los Alamos, NM Charles Babbage Institute The Center for the History of Information Processing University of Minnesota, Minneapolis Copyright, Charles Babbage Institute 1 Nicholas C. Metropolis Interview 29 May 1987 Abstract Metropolis, the first director of computing services at Los Alamos National Laboratory, discusses John von Neumann's work in computing. Most of the interview concerns activity at Los Alamos: how von Neumann came to consult at the laboratory; his scientific contacts there, including Metropolis, Robert Richtmyer, and Edward Teller; von Neumann's first hands-on experience with punched card equipment; his contributions to shock-fitting and the implosion problem; interactions between, and comparisons of von Neumann and Enrico Fermi; and the development of Monte Carlo techniques. Other topics include: the relationship between Turing and von Neumann; work on numerical methods for non-linear problems; and the ENIAC calculations done for Los Alamos. 2 NICHOLAS C. METROPOLIS INTERVIEW DATE: 29 May 1987 INTERVIEWER: William Aspray LOCATION: Los Alamos, NM ASPRAY: This is an interview on the 29th of May, 1987 in Los Alamos, New Mexico with Dr. Nicholas Metropolis. Why don't we begin by having you tell me something about when you first met von Neumann and what your relationship with him was over a period of time? METROPOLIS: Well, he first came to Los Alamos on the invitation of Dr. Oppenheimer. I don't remember quite when the date was that he came, but it was rather early on. It may have been the fall of 1943. -
Doing Mathematics on the ENIAC. Von Neumann's and Lehmer's
Doing Mathematics on the ENIAC. Von Neumann's and Lehmer's different visions.∗ Liesbeth De Mol Center for Logic and Philosophy of Science University of Ghent, Blandijnberg 2, 9000 Gent, Belgium [email protected] Abstract In this paper we will study the impact of the computer on math- ematics and its practice from a historical point of view. We will look at what kind of mathematical problems were implemented on early electronic computing machines and how these implementations were perceived. By doing so, we want to stress that the computer was in fact, from its very beginning, conceived as a mathematical instru- ment per se, thus situating the contemporary usage of the computer in mathematics in its proper historical background. We will focus on the work by two computer pioneers: Derrick H. Lehmer and John von Neumann. They were both involved with the ENIAC and had strong opinions about how these new machines might influence (theoretical and applied) mathematics. 1 Introduction The impact of the computer on society can hardly be underestimated: it affects almost every aspect of our lives. This influence is not restricted to ev- eryday activities like booking a hotel or corresponding with friends. Science has been changed dramatically by the computer { both in its form and in ∗The author is currently a postdoctoral research fellow of the Fund for Scientific Re- search { Flanders (FWO) and a fellow of the Kunsthochschule f¨urMedien, K¨oln. 1 its content. Also mathematics did not escape this influence of the computer. In fact, the first computer applications were mathematical in nature, i.e., the first electronic general-purpose computing machines were used to solve or study certain mathematical (applied as well as theoretical) problems. -
The New Nuclear Forensics: Analysis of Nuclear Material for Security
THE NEW NUCLEAR FORENSICS Analysis of Nuclear Materials for Security Purposes edited by vitaly fedchenko The New Nuclear Forensics Analysis of Nuclear Materials for Security Purposes STOCKHOLM INTERNATIONAL PEACE RESEARCH INSTITUTE SIPRI is an independent international institute dedicated to research into conflict, armaments, arms control and disarmament. Established in 1966, SIPRI provides data, analysis and recommendations, based on open sources, to policymakers, researchers, media and the interested public. The Governing Board is not responsible for the views expressed in the publications of the Institute. GOVERNING BOARD Sven-Olof Petersson, Chairman (Sweden) Dr Dewi Fortuna Anwar (Indonesia) Dr Vladimir Baranovsky (Russia) Ambassador Lakhdar Brahimi (Algeria) Jayantha Dhanapala (Sri Lanka) Ambassador Wolfgang Ischinger (Germany) Professor Mary Kaldor (United Kingdom) The Director DIRECTOR Dr Ian Anthony (United Kingdom) Signalistgatan 9 SE-169 70 Solna, Sweden Telephone: +46 8 655 97 00 Fax: +46 8 655 97 33 Email: [email protected] Internet: www.sipri.org The New Nuclear Forensics Analysis of Nuclear Materials for Security Purposes EDITED BY VITALY FEDCHENKO OXFORD UNIVERSITY PRESS 2015 1 Great Clarendon Street, Oxford OX2 6DP, United Kingdom Oxford University Press is a department of the University of Oxford. It furthers the University’s objective of excellence in research, scholarship, and education by publishing worldwide. Oxford is a registered trade mark of Oxford University Press in the UK and in certain other countries © SIPRI 2015 The moral rights of the authors have been asserted All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted, in any form or by any means, without the prior permission in writing of SIPRI, or as expressly permitted by law, or under terms agreed with the appropriate reprographics rights organizations. -
Computer History a Look Back Contents
Computer History A look back Contents 1 Computer 1 1.1 Etymology ................................................. 1 1.2 History ................................................... 1 1.2.1 Pre-twentieth century ....................................... 1 1.2.2 First general-purpose computing device ............................. 3 1.2.3 Later analog computers ...................................... 3 1.2.4 Digital computer development .................................. 4 1.2.5 Mobile computers become dominant ............................... 7 1.3 Programs ................................................. 7 1.3.1 Stored program architecture ................................... 8 1.3.2 Machine code ........................................... 8 1.3.3 Programming language ...................................... 9 1.3.4 Fourth Generation Languages ................................... 9 1.3.5 Program design .......................................... 9 1.3.6 Bugs ................................................ 9 1.4 Components ................................................ 10 1.4.1 Control unit ............................................ 10 1.4.2 Central processing unit (CPU) .................................. 11 1.4.3 Arithmetic logic unit (ALU) ................................... 11 1.4.4 Memory .............................................. 11 1.4.5 Input/output (I/O) ......................................... 12 1.4.6 Multitasking ............................................ 12 1.4.7 Multiprocessing .........................................