2000 AMS-SIAM Wiener Prize
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The Mathematical Sciences at Clemson
BIOMATHEMATICS IS The Geometry of Biological Time m Arthur Winfree, Purdue University The Geometry of Biological Time explains periodic processes in living systems >< and their nonliving analogues in the abstract terms of systems theory. Emphasis is on phase singularities, waves, and mutual synchronization in -n tissues composed of many clocklike units. Also provided are detailed de- )5-._U scriptions of the most commonly used experimental systems, such as electrical oscillations and waves, circadian clocks, the cell division cycle, and the crystal-like regularities observed in the regeneration of severed limbs. z No theoretical background is assumed: required notions are introduced through an extensive collection of illustrations and easily understood o examples. 1979/approx. 576 pp./290 lllus./Cioth $32.00 _ (Biomathematics. Volume 8) ISBN 0-387-09373-7 z Mathematical Population Genetics G) Warren J. Ewens, University of Pennsylvania, Philadelphia Presents the mathematical theory of population genetics with emphasis on those aspects relevant to evolutionary studies. The opening chapter pro- vides an excellent general historical and biological background. Subsequent chapters treat deterministic and stochastic models, discrete and continuous time processes, theory concerning classical and molecular aspects, and one, two, and many loci in a concise and comprehensive manner, with ample references to additional literature. An essential working guide for population geneticists interested in the mathematical foundations of their field and mathematicians involved in genetic evolutionary processes. 1979/ approx. 330 pp./ 4111us/17 Tables/ Cloth $32.00 (Biomathematics. Volume 9) ISBN 0-387-09577-2 Diffusion and Ecological Problems: M~thematical Models Akira Okubo, State University of New York, Stony Brook The first comprehensive book on mathematical models of diffusion in an ecological context. -
Table of Contents
Table of contents Table of contents..............................................................................................................................1 A Word from the Director................................................................................................................3 Presenting the CRM.........................................................................................................................5 Personnel..........................................................................................................................................6 Scientific Personnel..........................................................................................................................7 Members......................................................................................................................................... 7 Postdoctoral Fellows........................................................................................................................ 8 Visitors ..........................................................................................................................................10 Management................................................................................................................................... 12 Bureau...........................................................................................................................................12 Advisory Committee.......................................................................................................................12 -
Implicit Particle Filters for Data Assimilation Alexandre Chorin, Matthias Morzfeldand Xuemin Tu
Communications in Applied Mathematics and Computational Science IMPLICIT PARTICLE FILTERS FOR DATA ASSIMILATION ALEXANDRE CHORIN, MATTHIAS MORZFELD AND XUEMIN TU vol. 5 no. 2 2010 mathematical sciences publishers COMM. APP. MATH. AND COMP. SCI. Vol. 5, No. 2, 2010 IMPLICIT PARTICLE FILTERS FOR DATA ASSIMILATION ALEXANDRE CHORIN, MATTHIAS MORZFELD AND XUEMIN TU Implicit particle filters for data assimilation update the particles by first choosing probabilities and then looking for particle locations that assume them, guiding the particles one by one to the high probability domain. We provide a detailed description of these filters, with illustrative examples, together with new, more general, methods for solving the algebraic equations and with a new algorithm for parameter identification. 1. Introduction There are many problems in science, for example in meteorology and economics, in which the state of a system must be identified from an uncertain equation supplemented by noisy data (see, for instance,[9; 22]). A natural model of this situation consists of an Ito stochastic differential equation (SDE): dx D f .x; t/ dt C g.x; t/ dw; (1) where x D .x1; x2;:::; xm/ is an m-dimensional vector, f is an m-dimensional vector function, g.x; t/ is an m by m matrix, and w is Brownian motion which encapsulates all the uncertainty in the model. In the present paper we assume for simplicity that the matrix g.x; t/ is diagonal. The initial state x.0/ is given and may be random as well. The SDE is supplemented by measurements bn at times tn, n D 0; 1;::: . -
Convergence in the Max Norm Elbridge Gerry Puckett
Communications in Applied Mathematics and Computational Science ON THE SECOND-ORDER ACCURACY OF VOLUME-OF-FLUID INTERFACE RECONSTRUCTION ALGORITHMS: CONVERGENCE IN THE MAX NORM ELBRIDGE GERRY PUCKETT vol. 5 no. 1 2010 mathematical sciences publishers COMM. APP. MATH. AND COMP. SCI. Vol. 5, No. 1, 2010 ON THE SECOND-ORDER ACCURACY OF VOLUME-OF-FLUID INTERFACE RECONSTRUCTION ALGORITHMS: CONVERGENCE IN THE MAX NORM ELBRIDGE GERRY PUCKETT Given a two times differentiable curve in the plane, I prove that — using only the volume fractions associated with the curve — one can construct a piecewise linear approximation that is second-order in the max norm. I derive two parame- ters that depend only on the grid size and the curvature of the curve, respectively. When the maximum curvature in the 3 by 3 block of cells centered on a cell through which the curve passes is less than the first parameter, the approxima- tion in that cell will be second-order. Conversely, if the grid size in this block is greater than the second parameter, the approximation in the center cell can be less than second-order. Thus, this parameter provides an a priori test for when the interface is under-resolved, so that when the interface reconstruction method is coupled to an adaptive mesh refinement algorithm, this parameter may be used to determine when to locally increase the resolution of the grid. 1. Introduction In this article I study the interface reconstruction problem for a volume-of-fluid method in two space dimensions. Let 2 R2 denote a simply connected domain and let z.s/ D .x.s/; y.s//, where s is arc length, denote a curve in . -
2000S 1900S 1800S 1700S 1600S 1500S 1400S
2000s Hala Shehadeh Alexis Stevens Cassie Williams Katie Quertermous Minah Oh Eva Strawbridge Josh Ducey Lihua Chen Brant Jones Edwin O’Shea Ling Xu Nusrat Jahan Anthony Tongen Roger Thelwell Elizabeth Brown Elizabeth Arnold Brian Walton Jason Rosenhouse Hasan Hamdan LouAnn Lovin Laura Taalman Rebecca Field Samantha Prins Jay Gopalakrishnan Timothy Hansen Yuhong Yang Sara Billey Rekha Thomas Steve Garren Jesse Wilkins Jeffrey Achter David Chopp Ed Lee Len Van Wyk James Liu Jane Harvill Debra Warne Paul Warne Jeanne Fitzgerald Philippe Loustaunau Peter Kohn Burt Totaro Dave Pruett Carl Droms Dorothy Wallace Bernd Sturmfels John Marafino Jim Sochacki Peter Sin Ching−Li Chai Joseph Watkins James Sethian John Nolan Craig Squier Dave Carothers Adrian Raftery Andrew Barron Chuck Cunningham Wesley Johnson Richard Smith Robert Kohn Hermann Fasel Joseph Pasciak Juergen Bokowski John Spurrier Ed Parker William Pardon Howard Newton Ching−Yuan Chiang Gary Peterson Robert Lax Akihiro Kanamori Craig Benham Cameron Gordon James Wilson Paul Deheuvels John Klippert Mark Tepley Audrey Terras Michael Collins Paul DuChateau Cornelius Horgan Thomas Cover Carter Lyons Adrian Mathias Thomas Kriete Jacques Lewin Thomas Kurtz Alexandre Chorin Loren Pitt John George Ronald Grimmer Charles Ziegenfus Spencer Dickson William Adams Joerg Wills John Hewett John McMillan John Hudson Jon May Howard Taylor Frederick Almgren James Simmonds Kenneth Travers Steven Kleiman James Rovnyack Ronald Jensen John Stallings Paul Waltman Norman Abramson J.J. Malone David Mumford Gilbert -
The Development of Nonlinear Science£
The development of nonlinear science£ Alwyn Scott Department of Mathematics University of Arizona Tucson, Arizona USA August 11, 2005 Abstract Research activities in nonlinear science over the past three centuries are reviewed, paying particular attention to the explosive growth of interest in chaos, solitons, and reaction-diffusion phenomena that occurred during the 1970s and considering whether this explosion was an example of a “sci- entific revolution” or Gestalt-like “paradigm shift” as proposed by Thomas Kuhn in the 1962. The broad structure of modern nonlinear science is sketched and details of developments in several areas of nonlinear research are presented, including cosmology, theories of matter, quantum theory, chemistry and biochemistry, solid-state physics, electronics, optics, hydro- dynamics, geophysics, economics, biophysics and neuroscience. It is con- cluded that the emergence of modern nonlinear science as a collective in- terdisciplinary activity was a Kuhnian paradigm shift which has emerged from diverse areas of science in response to the steady growth of comput- ing power over the past four decades and the accumulation of knowledge about nonlinear methods, which eventually broke through the barriers of balkanization. Implications of these perspectives for twentyfirst-century research in biophysics and in neuroscience are discussed. PACS: 01.65.+g, 01.70.+w, 05.45.-a £ Copyright c 2005 by Alwyn C. Scott. All rights reserved. 1 Contents 1 Introduction 6 1.1 What is nonlinear science? ...................... 6 1.2 An explosion of activity . ...................... 10 1.3 What caused the changes? ...................... 12 1.4 Three trigger events .......................... 16 2 Fundamental phenomena of nonlinear science 18 2.1 Chaos theory . -
Leroy P. Steele Prize for Mathematical Exposition 1
AMERICAN MATHEMATICAL SOCIETY LEROY P. S TEELE PRIZE FOR MATHEMATICAL EXPOSITION The Leroy P. Steele Prizes were established in 1970 in honor of George David Birk- hoff, William Fogg Osgood, and William Caspar Graustein and are endowed under the terms of a bequest from Leroy P. Steele. Prizes are awarded in up to three cate- gories. The following citation describes the award for Mathematical Exposition. Citation John B. Garnett An important development in harmonic analysis was the discovery, by C. Fefferman and E. Stein, in the early seventies, that the space of functions of bounded mean oscillation (BMO) can be realized as the limit of the Hardy spaces Hp as p tends to infinity. A crucial link in their proof is the use of “Carleson measure”—a quadratic norm condition introduced by Carleson in his famous proof of the “Corona” problem in complex analysis. In his book Bounded analytic functions (Pure and Applied Mathematics, 96, Academic Press, Inc. [Harcourt Brace Jovanovich, Publishers], New York-London, 1981, xvi+467 pp.), Garnett brings together these far-reaching ideas by adopting the techniques of singular integrals of the Calderón-Zygmund school and combining them with techniques in complex analysis. The book, which covers a wide range of beautiful topics in analysis, is extremely well organized and well written, with elegant, detailed proofs. The book has educated a whole generation of mathematicians with backgrounds in complex analysis and function algebras. It has had a great impact on the early careers of many leading analysts and has been widely adopted as a textbook for graduate courses and learning seminars in both the U.S. -
Parameter Estimation by Implicit Sampling Matthias Morzfeld,Xuemin Tu, Jon Wilkeningand Alexandre J.Chorin
Communications in Applied Mathematics and Computational Science PARAMETER ESTIMATION BY IMPLICIT SAMPLING MATTHIAS MORZFELD,XUEMIN TU, JON WILKENING AND ALEXANDRE J. CHORIN vol. 10 no. 2 2015 msp COMM. APP. MATH. AND COMP. SCI. Vol. 10, No. 2, 2015 dx.doi.org/10.2140/camcos.2015.10.205 msp PARAMETER ESTIMATION BY IMPLICIT SAMPLING MATTHIAS MORZFELD, XUEMIN TU, JON WILKENING AND ALEXANDRE J. CHORIN Implicit sampling is a weighted sampling method that is used in data assim- ilation to sequentially update state estimates of a stochastic model based on noisy and incomplete data. Here we apply implicit sampling to sample the posterior probability density of parameter estimation problems. The posterior probability combines prior information about the parameter with information from a numerical model, e.g., a partial differential equation (PDE), and noisy data. The result of our computations are parameters that lead to simulations that are compatible with the data. We demonstrate the usefulness of our implicit sampling algorithm with an example from subsurface flow. For an efficient implementation, we make use of multiple grids, BFGS optimization coupled to adjoint equations, and Karhunen–Loève expansions for dimensional reduction. Several difficulties of Markov chain Monte Carlo methods, e.g., estimation of burn-in times or correlations among the samples, are avoided because the implicit samples are independent. 1. Introduction We wish to compute a set of parameters θ, an m-dimensional vector, so that simulations with a numerical model that require these parameters are compatible with data z (a k-dimensional vector) we have collected. We assume that some information about the parameter is available before we collect the data and this information is summarized in a prior probability density function (pdf) p(θ/. -
Acknowledgment of Reviewers, 2009
Proceedings of the National Academy ofPNAS Sciences of the United States of America www.pnas.org Acknowledgment of Reviewers, 2009 The PNAS editors would like to thank all the individuals who dedicated their considerable time and expertise to the journal by serving as reviewers in 2009. Their generous contribution is deeply appreciated. A R. Alison Adcock Schahram Akbarian Paul Allen Lauren Ancel Meyers Duur Aanen Lia Addadi Brian Akerley Phillip Allen Robin Anders Lucien Aarden John Adelman Joshua Akey Fred Allendorf Jens Andersen Ruben Abagayan Zach Adelman Anna Akhmanova Robert Aller Olaf Andersen Alejandro Aballay Sarah Ades Eduard Akhunov Thorsten Allers Richard Andersen Cory Abate-Shen Stuart B. Adler Huda Akil Stefano Allesina Robert Andersen Abul Abbas Ralph Adolphs Shizuo Akira Richard Alley Adam Anderson Jonathan Abbatt Markus Aebi Gustav Akk Mark Alliegro Daniel Anderson Patrick Abbot Ueli Aebi Mikael Akke David Allison David Anderson Geoffrey Abbott Peter Aerts Armen Akopian Jeremy Allison Deborah Anderson L. Abbott Markus Affolter David Alais John Allman Gary Anderson Larry Abbott Pavel Afonine Eric Alani Laura Almasy James Anderson Akio Abe Jeffrey Agar Balbino Alarcon Osborne Almeida John Anderson Stephen Abedon Bharat Aggarwal McEwan Alastair Grac¸a Almeida-Porada Kathryn Anderson Steffen Abel John Aggleton Mikko Alava Genevieve Almouzni Mark Anderson Eugene Agichtein Christopher Albanese Emad Alnemri Richard Anderson Ted Abel Xabier Agirrezabala Birgit Alber Costica Aloman Robert P. Anderson Asa Abeliovich Ariel Agmon Tom Alber Jose´ Alonso Timothy Anderson Birgit Abler Noe¨l Agne`s Mark Albers Carlos Alonso-Alvarez Inger Andersson Robert Abraham Vladimir Agranovich Matthew Albert Suzanne Alonzo Tommy Andersson Wickliffe Abraham Anurag Agrawal Kurt Albertine Carlos Alos-Ferrer Masami Ando Charles Abrams Arun Agrawal Susan Alberts Seth Alper Tadashi Andoh Peter Abrams Rajendra Agrawal Adriana Albini Margaret Altemus Jose Andrade, Jr. -
Twenty-Eighth Annual Report of The
National Science Foundation Twenty-Eighth Annual Report for Fiscal Year 1978 For sale by the Superintendent of Documents, U.S. Government Printing Office Washington, D.C. 20402 - Price $3.25 Stock Number 038-000-00407-7 Letter of Transmittal Washington, D.C. DEAR MR. PRESIDENT: I have the honor to transmit herewith the Annual Report for Fiscal Year 1978 of the National Science Foundation for submission to the Congress as required by the National Science Foundation Act of 1950. Respectfully, Richard C. Atkinson Director, National Science foundation The Honorable The President of ihe United Slates Contents Page Director's Statement vii Mathematical and Physical Sciences, and Engineering 1 Physics 2 Chemistry 6 Mathematical and Computer Sciences 10 Engineering 16 Materials Research 21 Astronomical, Atmospheric, Earth, and Ocean Sciences 29 Astronomy 31 Atmospheric Sciences 39 Earth Sciences 45 Ocean Sciences 51 Polar Programs 56 Biological, Behavioral, and Social Sciences 61 Physiology, Cellular, and Molecular Biology 62 Behavioral and Neural Sciences 67 Environmental Biology 69 Social Sciences 73 Science Education '77 Science Education Resources Improvement 77 Science Education Development and Research 81 Scientific Personnel Improvement 86 Science and Society 91 Applied Science and Research Applications 97 Problem Analysis 98 Integrated Basic Research 98 Applied Research 99 Problem-Focused Research Applications 101 Intergovernmental Science and Public Technology 105 Scientific, Technological, and International Affairs 109 Policy Research and Analysis 110 Science Resources Studies 112 NSF Planning and Evaluation 115 Information Science and Technology 116 International Programs , 118 Appendices A. National Science Board, NSF Staff, Advisory Committees and Panels 121 B. Patents and Inventions Resulting from Activities Supported by the National Science Foundation 138 C. -
A Volume-Of-Fluidinterface Reconstruction Algorithm That
Communications in Applied Mathematics and Computational Science A VOLUME-OF-FLUID INTERFACE RECONSTRUCTION ALGORITHM THAT IS SECOND-ORDER ACCURATE IN THE MAX NORM ELBRIDGE GERRY PUCKETT vol. 5 no. 2 2010 mathematical sciences publishers COMM. APP. MATH. AND COMP. SCI. Vol. 5, No. 2, 2010 A VOLUME-OF-FLUID INTERFACE RECONSTRUCTION ALGORITHM THAT IS SECOND-ORDER ACCURATE IN THE MAX NORM ELBRIDGE GERRY PUCKETT In an article recently published in this journal the author proved there exists a two-dimensional, volume-of-fluid interface reconstruction method that is second- order accurate in the max norm. However, that article did not include an example of such an algorithm. This article contains a description of a two-dimensional, volume-of-fluid interface reconstruction method that is second-order accurate in the max norm, provided the curve that one is reconstructing is two times continuously differentiable and the length of the sides of the square grid cells is less than a constant divided by the maximum of the absolute value of the curvature of the interface. A computation made with this algorithm is presented that demonstrates the convergence rate is second-order, as expected. 1. Introduction Let 2 R2 denote a two-dimensional computational domain and take an oriented curve in parametrized by z.s/ D .x.s/; y.s//, where 0 ≤ s ≤ send is arc length. Let L be a characteristic length of the computational domain . Cover with a grid consisting of square cells each of side 1x ≤ L and let 1x h D (1) L be a dimensionless parameter that represents the size of a grid cell. -
Final Program
Final Program Held jointly with the SIAM Workshop on Network Science (NS19) Sponsored by the SIAM Activity Group on Dynamical Systems This activity group provides a forum for the exchange of ideas and information between mathematicians and applied scientists whose work involves dynamical systems. The goal of this group is to facilitate the development and application of new theory and methods of dynamical systems. The techniques in this area are making major contributions in many areas, including biology, nonlinear optics, fluids, chemistry, and mechanics. SIAM Events Mobile App Scan the QR code with any QR reader and download the TripBuilder EventMobile™ app to your iPhone, iPad, iTouch or Android mobile device. You can also visit http://www.tripbuildermedia.com/apps/siamevents Society for Industrial and Applied Mathematics 3600 Market Street, 6th Floor Philadelphia, PA 19104-2688 U.S. Telephone: +1-215-382-9800 Fax: +1-215-386-7999 Conference E-mail: [email protected] • Conference Web: www.siam.org/meetings/ Membership and Customer Service: (800) 447-7426 (U.S. & Canada) or +1-215-382-9800 (worldwide) https://www.siam.org/conferences/CM/Main/ds19 https://www.siam.org/conferences/CM/Main/ns19 2 SIAM Conference on Dynamical Systems and SIAM Workshop on Network Science Table of Contents Conference Themes Hotel Check-in and Program-At-A-Glance… The scope of this conference encompasses Check-out Times ..........................See separate handout theoretical, computational and experimental Check-in time is 4:00 p.m. research on dynamical