Deep Learning In Computational Mechanics de Kollmannsberger, Stefan Format Broché

Deep Learning in Computational Mechanics - Kollmannsberger, Stefan

Format: Broché

Note : 0

Soyez le premier à donner un avis

Vous en avez un à vendre ?

Vendez-le-vôtre

83,21 €

Produit Neuf

Ou 20,80 € /mois

Livraison à 0,01 €
Livré entre le 12 et le 26 mai

Voir les modes de livraison

PRO Vendeur favori

4,8/5 sur + de 1 000 ventes

Expédition rapide et soignée depuis l`Angleterre - Délai de livraison: entre 10 et 20 jours ouvrés.

Vous avez choisi le retrait chez le vendeur à

Payez directement sur Rakuten (CB, PayPal, 4xCB...)
Récupérez le produit directement chez le vendeur
Rakuten vous rembourse en cas de problème

Terminer ma commande

Gratuit et sans engagement

Félicitations !

Nous sommes heureux de vous compter parmi nos membres du Club Rakuten !

TROUVER UN MAGASIN

Adresse

Retour

Horaires

Note :

Avis sur Deep Learning In Computational Mechanics de Kollmannsberger, Stefan Format Broché - Livre Encyclopédies, Dictionnaires

Note : 0 0 avis sur Deep Learning In Computational Mechanics de Kollmannsberger, Stefan Format Broché - Livre Encyclopédies, Dictionnaires

Donnez votre avis

Donnez votre avis sur ce produit

Votre note * : 1/5 2/5 3/5 4/5 5/5 Veuillez donner une note au produit

Le titre de votre avis* : Saisissez un titre

Quelques conseils :

Donnez le ton de votre avis, résumez-le en quelques mots qui apparaîtront comme le titre de votre avis.

Votre avis* : (40 caractères minimum) Saisissez un avis

Quelques conseils :

N'hésitez pas à mettre en avant les caractéristiques de ce produit, n'oubliez pas de mentionner ses avantages et/ou ses défauts éventuels. Décrivez précisément, argumentez, soyez convaincant !

Annuler

Les avis publiés font l'objet d'un contrôle automatisé de Rakuten.

Produits similaires

Présentation Deep Learning In Computational Mechanics de Kollmannsberger, Stefan Format Broché
- Livre Encyclopédies, Dictionnaires

Livre Encyclopédies, Dictionnaires - Kollmannsberger, Stefan - 01/08/2022 - Broché - Langue : Anglais

Auteur(s) : Kollmannsberger, Stefan - Herrmann, Leon - Jokeit, Moritz - D'Angella, Davide

Editeur : Springer International Publishing Ag

Langue : Anglais

Parution : 01/08/2022

Format : Moyen, de 350g à 1kg

Nombre de pages : 112

Expédition : 184

Dimensions : 23.5 x 15.5 x 0.7

ISBN : 303076589X

Résumé :
This book provides a first course on deep learning in computational mechanics. The book starts with a short introduction to machine learning's fundamental concepts before neural networks are explained thoroughly. It then provides an overview of current topics in physics and engineering, setting the stage for the book's main topics: physics-informed neural networks and the deep energy method. The idea of the book is to provide the basic concepts in a mathematically sound manner and yet to stay as simple as possible. To achieve this goal, mostly one-dimensional examples are investigated, such as approximating functions by neural networks or the simulation of the temperature's evolution in a one-dimensional bar. Each chapter contains examples and exercises which are either solved analytically or in PyTorch, an open-source machine learning framework for python. ...

Biographie:

Stefan Kollmannsberger is an adjunct teaching professor at the Technical University of Munich (TUM) and leads the research group Simulation in Applied Mechanics at the Chair of Computational Modeling and Simulation at TUM. He graduated in Civil Engineering and spent some years as an engineer in the industry before devoting himself to Computational Mechanics in Munich. He is dedicated to teaching, science, and, of course, his family. Stefan loved to design this lecture book together with Moritz, Davide and Leon. He uses it as a basis for the introductory course in the field of Artificial Intelligence in Computational Mechanics which he teaches among other subjects at TUM.

Davide D'Angella grew up in a small Italian peninsula dividing the Venetian Lagoon from the Adriatic Sea. After his high-school graduation, being firmly convinced he did not want to study any longer, he worked a couple of years as a computer programmer. This experience made him eagerto deepen his theoretical understanding of the subject, giving him the motivation to continue his studies. He headed to the University of Milan, where he obtained a bachelor's degree in computer science. After a short Au Pair experience in Germany, he moved to Munich, where he got his master's degree in Computational Science and Engineering at the Technical University of Munich. Davide is now a research associate and Ph.D. candidate at the same university in the research group Simulation in Applied Mechanics at the chair of Computational Modeling and Simulation. As a product of the Mauerfall, Moritz Jokeit grew up in the non-existing town of Bielefeld and the alpine foothills near Rosenheim. Following his bachelor's degree in Civil Engineering, he studied Computational Mechanics at the Technical University of Munich (TUM) and the Polytechnic University of Catalonia (UPC). His passion for deep learning and computational mechanics was transformed into a master thesis thatlaid the groundwork for this lecture book. After his graduation he continued his research at the Chair of Computational Modeling and Simulation. He is now a doctoral candidate at the Institute for Biomechanics at the ETH Z?rich. Leon Herrmann has a very diverse background, born in South Africa and growing up in seven different countries. He did his bachelor studies in Mechanical Engineering at the Technical University of Denmark (DTU) and then continued his studies in Computational Mechanics at the Technical University of Munich (TUM). Additionally, being a part of the Bavarian Graduate School of Engineering (BGCE) provided him with important insights into the academic world, which he is very grateful for. His main study and research focus has been in finite element methods and the simulation of fracture in composite materials. He is now working in the field of artificial intelligence in computational mechanics.

...

Sommaire:
This book provides a first course on deep learning in computational mechanics. The book starts with a short introduction to machine learning's fundamental concepts before neural networks are explained thoroughly. It then provides an overview of current topics in physics and engineering, setting the stage for the book's main topics: physics-informed neural networks and the deep energy method. The idea of the book is to provide the basic concepts in a mathematically sound manner and yet to stay as simple as possible. To achieve this goal, mostly one-dimensional examples are investigated, such as approximating functions by neural networks or the simulation of the temperature's evolution in a one-dimensional bar. Each chapter contains examples and exercises which are either solved analytically or in PyTorch, an open-source machine learning framework for python. ...

Détails de conformité du produit

Consulter les détails de conformité de ce produit (

Personne responsable dans l'UE

)

Deep Learning in Computational Mechanics - Kollmannsberger, Stefan

Avis sur Deep Learning In Computational Mechanics de Kollmannsberger, Stefan Format Broché - Livre Encyclopédies, Dictionnaires

Présentation Deep Learning In Computational Mechanics de Kollmannsberger, Stefan Format Broché - Livre Encyclopédies, Dictionnaires

Présentation Deep Learning In Computational Mechanics de Kollmannsberger, Stefan Format Broché
- Livre Encyclopédies, Dictionnaires