An Introduction to Computational Stochastic PDEs (Cambridge Texts in Applied Mathematics)

Catherine E. Powell Gabriel J. Lord

ISBN 10: 0521728525 / ISBN 13: 9780521728522

Editore: Cambridge University Press 2014-06-30, 2014

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Codice articolo 6666-IUK-9780521728522

Dati bibliografici

• Titolo: An Introduction to Computational Stochastic PDEs (Cambridge Texts in Applied Mathematics)
• Editore: Cambridge University Press 2014-06-30
• Data di pubblicazione: 2014
• Lingua: Inglese
• ISBN 10: 0521728525
• ISBN 13: 9780521728522
• Rilegatura: Paperback
• Condizione: New

Descrizione articolo

Riassunto

This book offers a practical presentation of stochastic partial differential equations arising in physical applications and their numerical approximation.

Informazioni sugli autori

Gabriel Lord is a Professor in the Maxwell Institute, Department of Mathematics, at Heriot-Watt University, Edinburgh. He has worked on stochastic PDEs and applications for the past ten years. He is the co-editor of Stochastic Methods in Neuroscience with C. Liang, has organised a number of international meetings in the field, and is principal investigator on the porous media processes and mathematics network funded by the Engineering and Physical Sciences Research Council (UK). He is a member of the Society for Industrial and Applied Mathematics, LMS, and EMS, as well as an Associate Editor for the SIAM Journal on Scientific Computing and the SIAM/ASA Journal on Uncertainty Quantification.

Catherine Powell is a Senior Lecturer in Applied Mathematics and Numerical Analysis at the University of Manchester. She has worked in the field of stochastic PDEs and uncertainty quantification for ten years. She has co-organised several conferences on the subject, and together with Tony Shardlow, initialised the annual NASPDE series of meetings (now in its sixth year). Currently, she is the principal investigator on an Engineering and Physical Sciences Research Council funded project on the 'Numerical Analysis of PDEs with Random Data'. She is a member of the Society for Industrial and Applied Mathematics and an Associate Editor for the SIAM/ASA Journal on Uncertainty Quantification.

Tony Shardlow has been working in the numerical analysis group at the University of Bath since 2012. Before that, he held appointments at the universities of Manchester, Durham, Oxford, and Minnesota. He completed his Ph.D. in Scientific Computing and Computational Mathematics at Stanford University in 1997.