Data-driven Modelling and Scientific Machine Learning in Continuum Physics

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Éditeur :

Springer

Paru le : 2024-07-29

This monograph takes the reader through recent advances in data-driven methods and machine learning for problems in science—specifically in continuum physics. It develops the foundations and details a number of scientific machine learning approaches to enrich current computational models of continuu...
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Éditeur

Collection
n.c

Parution
2024-07-29

Pages
230 pages

EAN papier
9783031620287

Auteur(s) du livre


Krishna Garikipati obtained his PhD at Stanford University in 1996, and after a few years of post-doctoral work, he joined the University of Michigan in 2000, rising to Professor in the Departments of Mechanical Engineering and Mathematics. Between 2016 and 2022, he served as the Director of the Michigan Institute for Computational Discovery & Engineering (MICDE). In January 2024 he moved to a a new position as Professor of Aerospace and Mechanical Engineering at University of Southern California. His research is in scientific machine learning and computational science, with applications drawn from biophysics, materials physics, mechanics and mathematical biology. He has been awarded the DOE Early Career Award for Scientists and Engineers, the Presidential Early Career Award for Scientists and Engineers (PECASE), and a Humboldt Research Fellowship. He is a fellow of the US Association for Computational Mechanics, and the International Association for Computational Mechanics, a Life Member of Clare Hall at University of Cambridge, and a visiting scholar in Computational Biology at the Flatiron Institute of the Simons Foundation.

Caractéristiques détaillées - droits

EAN PDF
9783031620294
Prix
137,14 €
Nombre pages copiables
2
Nombre pages imprimables
23
Taille du fichier
12585 Ko
EAN EPUB
9783031620294
Prix
137,14 €
Nombre pages copiables
2
Nombre pages imprimables
23
Taille du fichier
33270 Ko

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