Krishna garikipati (37 risultati)

Condizione: 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.…

Condizione: New. In English.

Condizione: New. pp. 296.

Condizione: New. pp. 296.

Buch. Condizione: 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. Condizione: 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.…

Condizione: New. In English.

Condizione: 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. Condizione: 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.…

Condizione: New. 2024th edition NO-PA16APR2015-KAP.

Taschenbuch. Condizione: 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.…

Condizione: New. 2024th edition NO-PA16APR2015-KAP.

Buch. Condizione: 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.…

Paperback. Condizione: Like New. LIKE NEW. SHIPS FROM MULTIPLE LOCATIONS. book.

Condizione: new. Questo è un articolo print on demand.

Condizione: new. Questo è un articolo print on demand.

Condizione: new. Questo è un articolo print on demand.

Condizione: new. Questo è un articolo print on demand.

Buch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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. 285 pp. Englisch.…

Taschenbuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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. 296 pp. Englisch.…

Condizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. State of current understanding, and the viewpoints of biologists, biophysicists and mechaniciansPart I Tissue mechanics: Experimental and computational investigation of viscoelasticity of native and engineered ligament and tendon, by J. Ma, H. Na.…

Gebunden. Condizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. State of current understanding, and the viewpoints of biologists, biophysicists and mechaniciansPart I Tissue mechanics: Experimental and computational investigation of viscoelasticity of native and engineered ligament and tendon, by J. Ma, H. Na.…

Condizione: New. Print on Demand pp. 296 49:B&W 6.14 x 9.21 in or 234 x 156 mm (Royal 8vo) Perfect Bound on White w/Gloss Lam.

Condizione: New. Print on Demand pp. 296 52:B&W 6.14 x 9.21in or 234 x 156mm (Royal 8vo) Case Laminate on White w/Gloss Lam.

Taschenbuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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. 244 pp. Englisch.…

Condizione: New. PRINT ON DEMAND pp. 296.

Condizione: New. PRINT ON DEMAND pp. 296.

Taschenbuch. Condizione: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Tissue Mechanics.- Experimental and Computational Investigation of Viscoelasticity of Native and Engineered Ligament and Tendon.- A Comparison of a Nonlinear and Quasilinear Viscoelastic Anisotropic Model for Fibrous Tissues.- Hysteretic Behavior of Ligaments and Tendons: Microstructural Analysis of Damage, Softening and Non-Recoverable Strain.- On Measuring Stress Distributions in Epithelia.- A Viscoelastic Anisotropic Model for Soft Collageneous Tissues Based on Distributed Fiber-Matrix Units.- Cell-substrate Interactions.- Chemical and Mechanical Micro-Diversity of the Extracellular Matrix.- Tissue-to-Cellular Deformation Coupling in Cell-Microintegrated Elastomeric Scaffolds.- Orientational Polarizability and Stress Response of Biological Cells.- Universal Temporal Response of Fibroblasts Adhering on Cyclically Stretched Substrates.- Mechanics of DNA.- Elastic and Electrostatic Model for DNA in Rotation-Extension Experiments.- Shape and Energetics of DNA Plectonemes.- Mechanics of Biopolymer Networks.- Constitutive Models for the Force-Extension Behavior of Biological Filaments.- Small Strain Topological Effects of Biopolymer Networks with Rigid Cross-Links.- Cell adhesion.- An Observation on Bell's Model for Molecular Bond Separation Under Force.- A Theoretical Study of the Thermodynamics and Kinetics of Focal Adhesion Dynamics.- Tension-Induced Growth of Focal Adhesions at Cell-Substrate Interface.- Pattern Formation and Force Generation by Cell Ensembles in a Filamentous Matrix.- Mechano-Chemical Coupling in Shell Adhesion.- Catch-to-Slip Bond Transition in Biological Bonds by Entropic and Energetic Elasticity.- Growth.- Dilation and Hypertrophy: A Cell-Based Continuum Mechanics Approach Towards Ventricular Growth and Remodeling.- A Morpho-Elastic Modelof Hyphal Tip Growth in Filamentous Organisms.- Extracellular Control of Limb Regeneration.- Poroelasticity of Bone.- Bone Composite Mechanics Related to Collagen Hydration State.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 296 pp. Englisch.…

Kartoniert / Broschiert. Condizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. 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 e.…

Buch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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. 230 pp. Englisch.…