標(biāo)題: Titlebook: Deep Learning in Computational Mechanics; An Introductory Cour Stefan Kollmannsberger,Davide D‘Angella,Leon Herrm Textbook 2021 The Editor( [打印本頁] 作者: 回憶錄 時間: 2025-3-21 17:58
書目名稱Deep Learning in Computational Mechanics影響因子(影響力)
書目名稱Deep Learning in Computational Mechanics影響因子(影響力)學(xué)科排名
書目名稱Deep Learning in Computational Mechanics網(wǎng)絡(luò)公開度
書目名稱Deep Learning in Computational Mechanics網(wǎng)絡(luò)公開度學(xué)科排名
書目名稱Deep Learning in Computational Mechanics被引頻次
書目名稱Deep Learning in Computational Mechanics被引頻次學(xué)科排名
書目名稱Deep Learning in Computational Mechanics年度引用
書目名稱Deep Learning in Computational Mechanics年度引用學(xué)科排名
書目名稱Deep Learning in Computational Mechanics讀者反饋
書目名稱Deep Learning in Computational Mechanics讀者反饋學(xué)科排名
作者: 清唱劇 時間: 2025-3-21 20:39
C. Z. Dong,P. Nordlander,T. E. Madeyhysics and engineering have also taken advantage of machine learning by tuning these methods for their purpose. This chapter starts with a general review and then describes combined models and surrogate models. The idea is to show how machine learning can be used in physics and engineering without diving into technical details.作者: 占線 時間: 2025-3-22 02:14
https://doi.org/10.1007/978-3-030-76587-3Computational Intelligence; Artificial Intelligence; Computational Mechanics; Neural Networks; Machine L作者: 奇思怪想 時間: 2025-3-22 07:40
978-3-030-76589-7The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl作者: ARBOR 時間: 2025-3-22 11:15
https://doi.org/10.1007/978-3-642-73728-2ter interest in areas other than computer science, such as physics and engineering. This chapter provides a brief overview of the recent developments in artificial intelligence. Furthermore, several ideas of different approaches using deep learning in computational mechanics are introduced. When tra作者: 壁畫 時間: 2025-3-22 15:00
Desorption Processes in Planetary Science this using data. This chapter gives an overview of the fundamental concepts, including the data structures, learning types, and the different machine learning tasks. Additionally, the gradient descent method is introduced to illustrate how many machine learning algorithms learn through experience. 作者: 壁畫 時間: 2025-3-22 20:44
R. L. Kurtz,R. Stockbauer,T. E. Madeys. ANNs serve as universal function approximators, meaning that a sufficiently complex neural network can learn almost any function in any dimension. This flexibility, combined with backpropagation and a learning algorithm, enables to learn unknown functions with an astonishing accuracy. This chapte作者: Indecisive 時間: 2025-3-22 21:25
C. Z. Dong,P. Nordlander,T. E. Madeyhysics and engineering have also taken advantage of machine learning by tuning these methods for their purpose. This chapter starts with a general review and then describes combined models and surrogate models. The idea is to show how machine learning can be used in physics and engineering without d作者: 變化 時間: 2025-3-23 01:40
D. Feldmann,J. Kutzner,K. H. Welgel equation, including the residual in the cost function. PINNs can be used for both solving and discovering differential equations. In this chapter, PINNs are illustrated with three one-dimensional examples. The first example shows how the displacement of a static bar can be computed. The temperatur作者: fulmination 時間: 2025-3-23 06:07 作者: 草率男 時間: 2025-3-23 12:06
Machine Learning in Physics and Engineering,hysics and engineering have also taken advantage of machine learning by tuning these methods for their purpose. This chapter starts with a general review and then describes combined models and surrogate models. The idea is to show how machine learning can be used in physics and engineering without diving into technical details.作者: 金桌活畫面 時間: 2025-3-23 16:10
Stefan Kollmannsberger,Davide D‘Angella,Leon HerrmIntroduces to the adaption of learning-based methods in the domain of computational mechanics.Presents fundamental concepts of Machine Learning, Neural Networks and their corresponding algorithms.Revi作者: 召集 時間: 2025-3-23 20:10 作者: SEVER 時間: 2025-3-24 01:43 作者: STIT 時間: 2025-3-24 05:53
1860-949X ing, Neural Networks and their corresponding algorithms.Revi.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 overvi作者: CLAY 時間: 2025-3-24 09:46 作者: Kinetic 時間: 2025-3-24 12:39
R. L. Kurtz,R. Stockbauer,T. E. Madeyated. The derivatives with respect to the networks’ input are also explained, as these are essential for the upcoming chapters on physics-informed neural networks and the deep energy method. Finally, an outlook on more advanced network architectures is provided.作者: 使長胖 時間: 2025-3-24 18:06 作者: FAWN 時間: 2025-3-24 20:56
Textbook 2021ental 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 作者: MAL 時間: 2025-3-25 02:48
Introduction,nsferring the artificial intelligence approaches from computer science to physics and engineering, the main obstacle is the lack of data. This difficulty is overcome by enforcing the underlying physics in the learning algorithms. Finally, the chapter presents the outline of the book to orientate the reader.作者: Rodent 時間: 2025-3-25 03:23
Physics-Informed Neural Networks,e evolution in a one-dimensional spatial domain is determined using the non-linear heat equation, using both a continuous and a discrete approach. Finally, the data-driven identification is illustrated with the static bar, where the cross-sectional stiffness is estimated from the displacement.作者: 天賦 時間: 2025-3-25 07:44
Deep Energy Method,r to handle singularities than with the PINNs. However, this approach cannot be used for the identification of differential equations. The method is illustrated with the same static bar example from Chap.?2, where the displacement is estimated.作者: 創(chuàng)造性 時間: 2025-3-25 13:50 作者: grotto 時間: 2025-3-25 16:05 作者: 柳樹；枯黃 時間: 2025-3-25 23:39 作者: 大包裹 時間: 2025-3-26 01:34 作者: 發(fā)源 時間: 2025-3-26 07:29
Introduction,ter interest in areas other than computer science, such as physics and engineering. This chapter provides a brief overview of the recent developments in artificial intelligence. Furthermore, several ideas of different approaches using deep learning in computational mechanics are introduced. When tra作者: 的闡明 時間: 2025-3-26 09:20
Fundamental Concepts of Machine Learning, this using data. This chapter gives an overview of the fundamental concepts, including the data structures, learning types, and the different machine learning tasks. Additionally, the gradient descent method is introduced to illustrate how many machine learning algorithms learn through experience. 作者: 使虛弱 時間: 2025-3-26 15:29
Neural Networks,s. ANNs serve as universal function approximators, meaning that a sufficiently complex neural network can learn almost any function in any dimension. This flexibility, combined with backpropagation and a learning algorithm, enables to learn unknown functions with an astonishing accuracy. This chapte作者: FATAL 時間: 2025-3-26 20:25 作者: 尋找 時間: 2025-3-26 22:03
Physics-Informed Neural Networks,l equation, including the residual in the cost function. PINNs can be used for both solving and discovering differential equations. In this chapter, PINNs are illustrated with three one-dimensional examples. The first example shows how the displacement of a static bar can be computed. The temperatur作者: 胰臟 時間: 2025-3-27 02:23 作者: 磨碎 時間: 2025-3-27 07:28
1860-949X ion 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.. .?.978-3-030-76589-7978-3-030-76587-3Series ISSN 1860-949X Series E-ISSN 1860-9503 作者: 懲罰 時間: 2025-3-27 11:11 作者: 值得贊賞 時間: 2025-3-27 17:09 作者: Scleroderma 時間: 2025-3-27 17:50 作者: elastic 時間: 2025-3-28 01:14
tion mixing of neutrinos. The neutrino experiments described were carried out mainly by Japanese researchers. All the kamiokande results, detector performances, and complete references are included. Experiments regarding the neutrino‘s mass are represented in the direct mass measurement, the double beta-decay978-4-431-67031-5978-4-431-67029-2作者: bile648 時間: 2025-3-28 04:02 作者: fulcrum 時間: 2025-3-28 06:54 作者: resistant 時間: 2025-3-28 13:39
