Krishna garikipati (9 Ergebnisse)

Zustand: New. 244 pp., hardcover, new. - If you are reading this, this item is actually (physically) in our stock and ready for shipment once ordered. We are not bookjackers. Buyer is responsible for any additional duties, taxes, or fees required by recipient's country. Photos available upon request.…

Zustand: New. In English.

Buch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - These are the proceedings of an IUTAM Symposium on Cellular, Molecular and Tissue Mechanics, held in the summer of 2008 at Woods Hole, Mass, USA. This groundbreaking meeting brought together mechanicians having an interest in biological systems, with biophysicists and biologists in order to address the mechanical basis of biology at the molecular, cellular and tissue scales. The meeting explored a wealth of phenomena in cell and molecular biology all of which display a breadth of mechanical influences that may seem surprising at first glance to the traditional mechanician. This arena for the application of methods developed in mechanics has not drawn as much attention as more traditional tissue biomechanics. However, biophysicists, and even biologists apply ideas of elasticity and structural mechanics quite widely in studying molecules and cells. Many critical functions of biomolecules and cells are fundamentally mechanical in origin, and the mechanics of many biomolecules demonstrate scaling laws that are non-classical to traditional structural mechanics and elasticity. This compilation of the scientific papers presented at the meeting will prove invaluable to researchers in the mechanics of biology as this field develops in the decades to come and takes its place as an acknowledged and central area in biology.…

Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - These are the proceedings of an IUTAM Symposium on Cellular, Molecular and Tissue Mechanics, held in the summer of 2008 at Woods Hole, Mass, USA. This groundbreaking meeting brought together mechanicians having an interest in biological systems, with biophysicists and biologists in order to address the mechanical basis of biology at the molecular, cellular and tissue scales. The meeting explored a wealth of phenomena in cell and molecular biology all of which display a breadth of mechanical influences that may seem surprising at first glance to the traditional mechanician. This arena for the application of methods developed in mechanics has not drawn as much attention as more traditional tissue biomechanics. However, biophysicists, and even biologists apply ideas of elasticity and structural mechanics quite widely in studying molecules and cells. Many critical functions of biomolecules and cells are fundamentally mechanical in origin, and the mechanics of many biomolecules demonstrate scaling laws that are non-classical to traditional structural mechanics and elasticity. This compilation of the scientific papers presented at the meeting will prove invaluable to researchers in the mechanics of biology as this field develops in the decades to come and takes its place as an acknowledged and central area in biology.…

Zustand: New. In English.

Taschenbuch. Zustand: Neu. IUTAM Symposium on Cellular, Molecular and Tissue Mechanics | Proceedings of the IUTAM symposium held at Woods Hole, Mass., USA, June 18-21, 2008 | Krishna Garikipati (u. a.) | Taschenbuch | IUTAM Bookseries | ix | Englisch | 2012 | Springer | EAN 9789400731134 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.…

Zustand: Good. This is an ex-library book and may have the usual library/used-book markings inside.This book has hardback covers. In good all round condition. No dust jacket. Please note the Image in this listing is a stock photo and may not match the covers of the actual item,600grams, ISBN:9783031620287. …

Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - 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 continuum physics, or to use the data generated by these models to infer more information on these problems. The perspective presented here is drawn from recent research by the author and collaborators. Applications drawn from the physics of materials or from biophysics illustrate each topic. Some elements of the theoretical background in continuum physics that are essential to address these applications are developed first. These chapters focus on nonlinear elasticity and mass transport, with particular attention directed at descriptions of phase separation. This is followed by a brief treatment of the finite element method, since it is the most widely used approach to solve coupled partial differential equations in continuum physics.With these foundations established, the treatment proceeds to a number of recent developments in data-driven methods and scientific machine learning in the context of the continuum physics of materials and biosystems. This part of the monograph begins by addressing numerical homogenization of microstructural response using feed-forward as well as convolutional neural networks. Next is surrogate optimization using multifidelitylearning for problems of phase evolution. Graph theory bears many equivalences to partial differential equations in its properties of representation and avenues for analysis as well as reduced-order descriptions--all ideas that offer fruitful opportunities for exploration. Neural networks, by their capacity for representation of high-dimensional functions, are powerful for scale bridging in physics--an idea on which we present a particular perspective in the context of alloys.One of the most compelling ideas in scientific machine learning is the identification of governing equations from dynamical data--another topic that we explore from the viewpoint of partial differential equations encoding mechanisms. This is followed by an examination of approaches to replace traditional, discretization-based solvers of partial differential equations with deterministic and probabilistic neural networks that generalize across boundary value problems. The monograph closes with a brief outlook on current emerging ideas in scientific machine learning.…

Buch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - 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 continuum physics, or to use the data generated by these models to infer more information on these problems. The perspective presented here is drawn from recent research by the author and collaborators. Applications drawn from the physics of materials or from biophysics illustrate each topic. Some elements of the theoretical background in continuum physics that are essential to address these applications are developed first. These chapters focus on nonlinear elasticity and mass transport, with particular attention directed at descriptions of phase separation. This is followed by a brief treatment of the finite element method, since it is the most widely used approach to solve coupled partial differential equations in continuum physics.With these foundations established, the treatment proceeds to a number of recent developments in data-driven methods and scientific machine learning in the context of the continuum physics of materials and biosystems. This part of the monograph begins by addressing numerical homogenization of microstructural response using feed-forward as well as convolutional neural networks. Next is surrogate optimization using multifidelitylearning for problems of phase evolution. Graph theory bears many equivalences to partial differential equations in its properties of representation and avenues for analysis as well as reduced-order descriptions--all ideas that offer fruitful opportunities for exploration. Neural networks, by their capacity for representation of high-dimensional functions, are powerful for scale bridging in physics--an idea on which we present a particular perspective in the context of alloys.One of the most compelling ideas in scientific machine learning is the identification of governing equations from dynamical data--another topic that we explore from the viewpoint of partial differential equations encoding mechanisms. This is followed by an examination of approaches to replace traditional, discretization-based solvers of partial differential equations with deterministic and probabilistic neural networks that generalize across boundary value problems. The monograph closes with a brief outlook on current emerging ideas in scientific machine learning.…