Geometric Partial Differential Equations - Part 2: Volume 22

ISBN-13: 9780444643056 / Angielski / Twarda / 2021 / 570 str.

Ricardo Horacio Nochetto; Andrea Bonito

Spis treści

1. Shape and topology optimization Grégoire Allaire, Charles Dapogny, and François Jouve

2. Optimal transport: discretization and algorithms Quentin Mérigot and Boris Thibert

3. Optimal control of geometric partial differential equations Michael Hintermüller and Tobias Keil

4. Lagrangian schemes for Wasserstein gradient flows Jose A. Carrillo, Daniel Matthes, and Marie-Therese Wolfram

5. The Q-tensor model with uniaxial constraint Juan Pablo Borthagaray and Shawn W. Walker

6. Approximating the total variation with finite differences or finite elements Antonin Chambolle and Thomas Pock

7. Numerical simulation and benchmarking of drops and bubbles Stefan Turek and Otto Mierka

8. Smooth multi-patch discretizations in isogeometric analysis Thomas J.R. Hughes, Giancarlo Sangalli, Thomas Takacs, and Deepesh Toshniwal

O autorze

Andrea Bonito is professor in the Department of Mathematics at Texas A&M University.

Together with Ricardo H. Nochetto they have more than forty years of experience in the variational formulation and approximation of a wide range of geometric partial differential equations (PDEs). Their work encompass fundamental studies of numerical PDEs: the design, analysis and implementation of efficient numerical algorithms for the approximation of PDEs; and their applications in modern engineering, science, and bio-medical problems. Ricardo H. Nochetto is professor in the Department of Mathematics and the Institute for Physical Science and Technology at the University of Maryland, College Park.

Together with Andrea Bonito they have more than forty years of experience in the variational formulation and approximation of a wide range of geometric partial differential equations (PDEs). Their work encompass fundamental studies of numerical PDEs: the design, analysis and implementation of efficient numerical algorithms for the approximation of PDEs; and their applications in modern engineering, science, and bio-medical problems.