Catastrophe Theory was introduced in the 1960s by the renowned Fields Medal mathematician Rene' Thom as a part of the general theory of local singularities. Since then it has found applications across many areas, including biology, economics, and chemical kinetics. By investigating the phenomena of bifurcation and chaos, Catastrophe Theory proved to be fundamental to the understanding of qualitative dynamics. This fully revised second edition includes two new chapters treating genericity and stability of unfoldings. The results on both these topics-which reveal the relevance and depth of Catastrophe Theory-have never before been available in a textbook. The first edition chapters have been revised and now include additional material. Most important is the incorporation of a theorem on the uniqueness of the residual singularity. With more than one hundred worked examples and exercises, the second edition retains the pragmatic approach of the first. The material is self-contained, and the style is as elementary as possible, assuming only knowledge of calculus and linear algebra at an advanced undergraduate level.
Table of Contents
Foreword, Preface to the First Edition, Preface to the Second Edition, 1 Nondegenerate Critical Points: The Morse Lemma, 2 The Fold and the Cusp, 3 Degenerate Critical Points: The Reduction Lemma, 4 Determinacy, 5 Codimension, 6 The Classification Theorem for Germs of Codimension at Most 4, 7 Unfoldings, 8 Transversality, 9 The Malgrange-Mather Preparation Theorem, 10 The Fundamental Theorem on Universal Unfoldings, 11 Genericity, 12 Stability, Appendix, References, Notation Index, Subject Index
DOMENICO P. L. CASTRIGIANO is Professor of Mathematics at the Technical University of Munich, where his research interests focus on problems of mathematical physics, and include real analysis and measure theory on topological spaces., SANDRA A. HAYES is Professor of Mathematics at the Technical University of Munich. Her research interests include higher-dimensional complex dynamical systems and chaotic time series analysis.