David Hyde and Alex Pothen introduce the Special Issue on Quantum Computing and survey some exciting technical developments.
Quantum algorithms can be understood through linear algebra and offer different tradeoffs than classical algorithms.
The fusion of machine learning and quantum computing has created an unprecedented avenue for innovation.
Quantum computing promises enormous computing power at low costs, marking a new chapter for financial mathematics.
It is quite difficult to fully harness the potential of quantum computers and outperform classical computers.
The High School Mathematical Contest in Modeling tasked students with two open-ended, real-world problems.
The Hackathon encouraged participants to tackle questions about worldwide energy data availability and solar resource potential.
Mark Levi draws connections between the conformal equivalence and electrical resistance of annular regions.
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2017 / xxii + 412 pages / Softcover / ISBN 978-1-611974-81-2 / List Price $99.00 / SIAM Member Price $69.30 / Order Code: CS15
Keywords: model reduction, approximation, proper orthogonal decomposition, reduced basis method, tensor methods
Contents Preface Index
Many physical, chemical, biomedical, and technical processes can be described by partial differential equations or dynamical systems. In spite of increasing computational capacities, many problems are of such high complexity that they are solvable only with severe simplifications, and the design of efficient numerical schemes remains a central research challenge. This book presents a tutorial introduction to recent developments in mathematical methods for model reduction and approximation of complex systems.
Model Reduction and Approximation: Theory and Algorithms
Audience This book is intended for researchers interested in model reduction and approximation, particularly graduate students and young researchers.
About the Author Peter Benner is director at the Max Planck Institute for Dynamics of Complex Technical Systems and head of the Computational Methods in Systems and Control Theory department. He is also a professor at TU Chemnitz and adjunct professor at Otto-von-Guericke University Magdeburg, and he is a member of the Research Center Dynamic Systems: Systems Engineering in Magdeburg. He serves on the editorial board of several scientific journals, including SIAM Journal on Matrix Analysis and Applications.
Mario Ohlberger is a full professor of applied mathematics and managing director of Applied Mathematics: Institute of Analysis and Numerics at the University of Münster. He is Associate Editor of five mathematical journals, including SIAM Journal on Scientific Computing. He is a member of the Center for Nonlinear Science, the Center for Multiscale Theory and Computation, and the Cluster of Excellence “Cells in Motion.”
Albert Cohen is a professor at Laboratoire Jacques Louis Lions, Université Pierre et Marie Curie, Paris, France. He was awarded the Vasil Popov Prize (1995), the Jacques Herbrant Prize (2000), and the Blaise Pascal Prize (2004), and he has been the PI of the ERC Advanced Grant BREAD since 2014. He has been an invited speaker at ICM 2002 (Numerical Analysis section) and plenary speaker at ICIAM 2007. He is the managing editor of Foundations of Computational Mathematics. He has been a senior member of Institut Universitaire de France since 2013.
Karen E. Willcox is Professor of Aeronautics and Astronautics at the Massachusetts Institute of Technology and Co-Director of the MIT Center for Computational Engineering. Prior to joining the faculty at MIT, she worked at Boeing Phantom Works with the Blended-Wing-Body aircraft design group. She has served in multiple leadership positions within AIAA and SIAM, including on the SIAM Activity Group on Computational Science and Engineering. She is Section Editor of SIAM Journal on Scientific Computing and Associate Editor of AIAA Journal.
ISBN 9781611974812
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