Computational Inverse Problems Governed by PDEs

· Other Titles in Applied Mathematics Book 211 · SIAM
Ebook
336
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About this ebook

This textbook focuses on computational methods for inverse problems that are governed by partial differential equations (PDEs). The author considers deterministic and Bayesian formulations and highlights how traditional tools from deterministic inversion can be integrated into solution methods for Bayesian inverse problems. Advanced topics such as post-optimality sensitivity analysis, optimal design of experiments, and Bayesian inversion under model uncertainty are also included.

Computational Inverse Problems Governed by PDEs offers readers a balance of theoretical and computational insight, an example-driven approach that provides an accessible presentation, and over 150 theoretical and computational exercises.

This book is intended for graduate students in applied mathematics and computational science. Researchers in computational mathematics, engineering, and the sciences will also find it a valuable resource.

This textbook focuses on computational methods for inverse problems that are governed by partial differential equations (PDEs). The author considers deterministic and Bayesian formulations and highlights how traditional tools from deterministic inversion can be integrated into solution methods fo...

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About the author

Alen Alexanderian is an associate professor of mathematics at North Carolina State University. His work focuses on computational methods for inverse problems governed by PDEs, optimal design of experiments for infinite-dimensional Bayesian inverse problems, and uncertainty quantification. His research is driven by applications in porous media flow and advection diffusion reaction processes modeling heat and mass transport, as well as applications in the life sciences.

Alen Alexanderian is an associate professor of mathematics at North Carolina State University. His work focuses on computational methods for inverse problems governed by PDEs, optimal design of experiments for infinite-dimensional Bayesian inverse problems, and uncertainty quantification. His res...

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