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Applied Computational Topology for Point Clouds and Sparse Timeseries Data Applied Computational Topology for Point Clouds and Sparse Timeseries Data Thesis by Melissa Yeung In Partial Fulfillment of the Requirements for the degree of Doctor of Philosophy CALIFORNIA INSTITUTE OF TECHNOLOGY Pasadena, California 2017 Defended June 8, 2016 ii c 2017 Melissa Yeung All rights reserved iii ACKNOWLEDGEMENTS The opportunity to devote uninterrupted time to graduate studies is one of great privilege. A graduate education often marks the beginning of a research career, where one endeavors to expand human knowledge and to make the world a better place for the next generation. Thus, first and foremost, I would like to thank the United States taxpayer, for contributing their hard-earned dollars so that I could have the opportunity to learn, to dream, and to discover. This dissertation would not have been possible without the support of my advisor, Mathieu Desbrun. Thank you for the freedom to explore, the op- portunity to find my own path, for letting me take the scenic route. I also owe much of my graduate experience to Dmitriy Morozov, who has been an indispensable guide and mentor in the world of computational topology and life. Thank you for teaching me the fundamentals—both in computa- tional topology and in high performance computing—and for ensuring that I would always have opportunities—opportunities to present my work, to learn computational topology from the luminaries, and to find belonging in one of the most supportive and welcoming mathematical communities. The journey that led me to a Ph.D. is improbable. I owe many of my successes along this journey to the numerous individuals who have supported and encouraged me along the way: Miklos Abert, David Beckstead, Collin Bleak, Karen Brucks, Danny Calegari, Gunnar Carlsson, Michael Dorff, Benson Farb, Erica Flapan, Deanna Haunsperger, Stephen Kennedy, David E. Keyes, Robert Maddock, J. Peter May, Konstantin Mischaikow, Paul J. Sally, Jr., Wilhelm Schlag, Stephen Skapek, Liz Stanhope, Glenn Stevens, Elizabeth Townsend, and Samuel L. Volchenboum. Thanks also to my fellow students for their friendship, support, and wisdom. This journey wouldn’t have been nearly as much fun without you. iv The work in this thesis was supported by a National Science Foundation Graduate Research Fellowship, a Department of Energy Computational Science Graduate Fellowship (DOE Grant # DE-FG02-97ER25308), the Department of Computing & Mathematical Sciences and the Department of Mathematics at the California Institute of Technology, Lawrence Berkeley National Laboratory, and Inria Sophia Antipolis. v ABSTRACT The proliferation of sensors and advancement of technology has led to the production and collection of unprecedented amounts of data in recent years. The data are often noisy, non-linear, and high-dimensional, and the effective- ness of traditional tools may be limited. Thus, the technological advances that enable the ubiquitous collection of data from the cosmological scale to the subatomic scale also necessitate the development of complementary tools that address the new nature of the data. Recently, there has been much interest in and success with developing topologically-motivated techniques for data analysis. These approaches are especially useful when a topological method is sensitive to large- and small-scale features that might not be detected by methods that require a level of geometric detail that is not provided by the data or by methods that may obscure geometric features, such as principal component analysis (PCA), multidimensional scaling (MDS), and cluster analysis. Our work explores topological data analysis through two frameworks. In the first part, we provide a tool for detecting material coherence from a set of spatially sparse particle trajectories via the study of a map induced on homology by the braid corresponding to the motion of particles. While the theory of coherent structures has received a great deal of attention and benefited from many advances in recent years, many of these techniques are limited when the data are sparse. Wedemonstrate through various examples that our work provides a practical and scalable tool for identifying coherent sets from a sparse set of particle trajectories using eigenanalysis. In the second part, we formalize the local-to-global structure captured by topology in the setting of point clouds. We extend existing tools in topologi- cal data analysis and provide a theoretical framework for studying topologi- cal features of a point cloud over a range of resolutions, enabling the analysis of topological features using statistical methods. We apply our tools to the analysis of high-dimensional geospatial sensor data and provide a statistic for quantifying climate anomalies. vii TABLE OF CONTENTS Acknowledgements.............................. iii Abstract..................................... v Table of Contents ............................... vii List of Illustrations .............................. ix Preface ..................................... xi Chapter I: Introduction............................ 1 Chapter II: Braids and Material Coherence................. 5 2.1 Introduction.............................. 5 2.2 Contributions............................. 7 2.3 Braid groups ............................. 9 2.4 Application to the analysis of flows................ 15 2.5 Examples ............................... 32 2.6 Discussion and future directions.................. 38 Chapter III: Topological Data Analysis................... 41 3.1 Introduction.............................. 41 3.2 Contributions............................. 42 3.3 Simplicial complexes......................... 44 3.4 Persistent homology......................... 46 3.5 Reeb graphs and Reeb spaces.................... 48 3.6 Mapper constructions........................ 55 3.7 Abstract mapper and hierarchical abstract mapper . 58 3.8 Application to the analysis of geospatial sensor data . 73 3.9 Discussion and future directions.................. 77 Nomenclature................................. 81 Bibliography.................................. 85 ix LIST OF ILLUSTRATIONS Number Page 2.1 Illustration of a tubular braid.................... 8 2.2 Illustration of σi ............................ 10 2.3 Illustration of µj ........................... 19 2.4 Illustration of σi(xi) ......................... 20 2.5 Illustration of σi(xi+1) ........................ 21 2.6 Illustration of the braid corresponding to σi,η1,η2 . 22 2.7 Covering space action of σi,η1,η2 ................... 23 2.8 Illustrations of Poincare´ sections for Aref’s blinking vortex flow 33 2.9 Eigenvectors of the Burau matrix for Aref’s blinking vortex flow 35 2.10 Attractors of the modified Duffing oscillator........... 36 2.11 Limit cycles of the modified Duffing oscillator.......... 37 2.12 Phase portrait of the modified Duffing oscillator......... 37 2.13 Eigenvectors of the Burau matrix for the modified Duffing oscillator................................ 38 2.14 Two types of initial conditions for the modified Duffing oscil- lator .................................. 39 3.1 Reeb graph of height function ................... 49 3.2 Step-by-step hierarchical mapper construction.......... 75 3.3 Top and cross-sectional views of the hierarchical mapper con- struction depicted in Figure 3.2................... 75 3.4 Mapper constructions for global sea surface temperatures. 78 3.5 1-dimensional persistence classes of hierarchical mapper con- struction of global sea surface temperatures ........... 79 xi PREFACE Work for this thesis is motivated by the conviction that our understanding of physical phenomena benefits greatly from a rich intersection of theory from geometry, topology, and dynamical systems. Advances in one field often inspire and bring forth paradigm shifts in another. When I began my Ph.D. studies, I was particularly interested in bringing this synchrony to computational methods for science and engineering applications. This thesis formalizes existing tools of computational topology and introduces new computational methods that allow us to detect dynamical structures from a topological lens. Topology, since its inception, has been studied and developed as an ap- plied tool for science. Henri Poincare,´ often credited as the inventor of algebraic topology, introduced an arsenal of topological techniques and concepts through a series of papers between 1892 and 1904 about the qual- itative theory of differential equations and the long-term stability of a me- chanical system. Poincare’s´ topological ideas have been hailed as “probably the greatest advance in celestial mechanics since Newton”, as his ideas “not only breathed new life into complex analysis and mechanics; they amounted to the creation of a major new field, algebraic topology” [1]. In the 1930s, Jean Leray (with Juliusz Schauder) developed a set of alge- braic tools, including the definition and basic properties of the “topological degree” of a map (related to Brouwer’s work), to study fluid dynamics [2]. Leray’s subsequent publications throughout the rest of the 1930s provided many applications of topological principles to fluid dynamics and PDEs. ∗ In recent years, there has been a renewed interest in the interaction of geom- etry, topology, dynamics, and computation. There is a growing realization ∗Initially, Leray’s interest in algebraic topology was tangential to his other mathematical interests. But in 1940, Leray became a prisoner of the Germans during World War II and spent five years in captivity in an officer’s camp, Oflag XVIIA, in Austria. “Leray feared
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