標(biāo)題: Titlebook: Computational Mechanics with Deep Learning; An Introduction Genki Yagawa,Atsuya Oishi Textbook 2023 The Editor(s) (if applicable) and The A [打印本頁] 作者: Anagram 時(shí)間: 2025-3-21 18:53
書目名稱Computational Mechanics with Deep Learning影響因子(影響力)
書目名稱Computational Mechanics with Deep Learning影響因子(影響力)學(xué)科排名
書目名稱Computational Mechanics with Deep Learning網(wǎng)絡(luò)公開度
書目名稱Computational Mechanics with Deep Learning網(wǎng)絡(luò)公開度學(xué)科排名
書目名稱Computational Mechanics with Deep Learning被引頻次
書目名稱Computational Mechanics with Deep Learning被引頻次學(xué)科排名
書目名稱Computational Mechanics with Deep Learning年度引用
書目名稱Computational Mechanics with Deep Learning年度引用學(xué)科排名
書目名稱Computational Mechanics with Deep Learning讀者反饋
書目名稱Computational Mechanics with Deep Learning讀者反饋學(xué)科排名
作者: 社團(tuán) 時(shí)間: 2025-3-21 23:14
Lecture Notes on Numerical Methods in Engineering and Scienceshttp://image.papertrans.cn/c/image/232678.jpg作者: 鞏固 時(shí)間: 2025-3-22 02:12 作者: Encoding 時(shí)間: 2025-3-22 05:44
978-3-031-11849-4The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl作者: 詩集 時(shí)間: 2025-3-22 11:41
https://doi.org/10.1007/978-1-349-03123-8eural network including the error back propagation algorithm, Sect.?. the convolutional neural networks, which have become the mainstream of deep learning in recent years, and Sect.?. compares various methods for accelerating the training process. Finally, Sect.?. describes regularization methods to作者: 組裝 時(shí)間: 2025-3-22 13:41 作者: 組裝 時(shí)間: 2025-3-22 20:37
https://doi.org/10.1007/978-1-349-19936-5llision between objects is one of them. In this chapter, we study an application of deep learning to the contact search process, which is indispensable in contact and collision analysis. In particular, we focus on the contact between two smooth contact surfaces. In Sect.?., the basics of the contact作者: cajole 時(shí)間: 2025-3-23 00:40
https://doi.org/10.1007/978-1-349-19936-5uss the application of deep learning to fluid dynamics problems. Section?. describes the basic equations of fluid dynamics, Sect.?. the basics of the finite difference method, one of the most popular methods for solving fluid dynamics problems, Sect.?. a practical example of a two-dimensional fluid 作者: Classify 時(shí)間: 2025-3-23 02:55
Organizing and Working in a Study Group,cy of element stiffness matrices (Sect.?.), finite element analysis using convolutional operations (Sect.?.), fluid analysis using variational autoencoders (Sect.?.), a zooming method using feedforward neural networks (Sect.?.), and an application of physics-informed neural networks to solid mechani作者: AER 時(shí)間: 2025-3-23 07:32
https://doi.org/10.1007/978-981-16-2305-9mputational Mechanics with Deep Learning” from the perspective of programming. Section . describes some programs in the field of computational mechanics used in the Data Preparation Phase, including three topics discussed in the case study: the element stiffness matrix by using numerical quadrature 作者: Comprise 時(shí)間: 2025-3-23 10:19
Computational Mechanics with Deep Learningeep learning in recent years based on the trend of the number of published papers on this topic, discussing how deep learning is applied to various fields in computational mechanics. In Sect. ., we review the research trends from the list of papers on computational mechanics with deep learning published since 2018.作者: 厭倦嗎你 時(shí)間: 2025-3-23 16:16 作者: visual-cortex 時(shí)間: 2025-3-23 19:34 作者: A保存的 時(shí)間: 2025-3-24 01:32 作者: Choreography 時(shí)間: 2025-3-24 04:45 作者: CLASP 時(shí)間: 2025-3-24 08:23
Springer Topics in Signal ProcessingIn this chapter, we discuss the classification of elements discussed in Chap. . with a real program implementation, where the entire process of an application of deep learning to computational mechanics is reproduced.作者: bourgeois 時(shí)間: 2025-3-24 10:40 作者: Capitulate 時(shí)間: 2025-3-24 17:44 作者: CLAIM 時(shí)間: 2025-3-24 21:05 作者: Protein 時(shí)間: 2025-3-25 01:22
Computer Programming for a Representative ProblemIn this chapter, we discuss the classification of elements discussed in Chap. . with a real program implementation, where the entire process of an application of deep learning to computational mechanics is reproduced.作者: 對(duì)手 時(shí)間: 2025-3-25 03:43
Computational Mechanics with Deep Learning978-3-031-11847-0Series ISSN 1877-7341 Series E-ISSN 1877-735X 作者: 不滿分子 時(shí)間: 2025-3-25 10:50 作者: Pillory 時(shí)間: 2025-3-25 11:53
Organizing and Working in a Study Group,cy of element stiffness matrices (Sect.?.), finite element analysis using convolutional operations (Sect.?.), fluid analysis using variational autoencoders (Sect.?.), a zooming method using feedforward neural networks (Sect.?.), and an application of physics-informed neural networks to solid mechanics (Sect.?.).作者: 紅腫 時(shí)間: 2025-3-25 18:06
1877-7341 e samples for practice.This book is intended for students, engineers, and researchers interested in both computational mechanics and deep learning. It presents the mathematical and computational foundations of Deep Learning with detailed mathematical formulas in an easy-to-understand manner. It also作者: 多樣 時(shí)間: 2025-3-25 21:49 作者: Outwit 時(shí)間: 2025-3-26 01:24
Mathematical Background for Deep Learningning in recent years, and Sect.?. compares various methods for accelerating the training process. Finally, Sect.?. describes regularization methods to suppress overtraining for improving performance of the trained neural networks.作者: bromide 時(shí)間: 2025-3-26 05:39 作者: Nausea 時(shí)間: 2025-3-26 11:01
https://doi.org/10.1007/978-1-349-19936-5dynamics simulation, Sect.?. the formulation of the application of deep learning to fluid dynamics problems, Sect.?. recurrent neural networks that are suitable for the time-dependent problems covered in this chapter, and finally, Sect.?. a real application of deep learning to the fluid dynamics simulation.作者: FLIC 時(shí)間: 2025-3-26 16:33
https://doi.org/10.1007/978-1-349-19936-5h as segmentation of NURBS-defined shapes, and conventional surface-to-surface contact search methods are taken, respectively. With these preparations, Sect.?. formulates a contact search method using deep learning, and finally, Sect.?. shows a numerical example作者: –DOX 時(shí)間: 2025-3-26 18:24 作者: MIR 時(shí)間: 2025-3-26 22:35
Contact Mechanics with Deep Learningh as segmentation of NURBS-defined shapes, and conventional surface-to-surface contact search methods are taken, respectively. With these preparations, Sect.?. formulates a contact search method using deep learning, and finally, Sect.?. shows a numerical example作者: 音樂等 時(shí)間: 2025-3-27 01:31
Bases for Computer Programmingscusses some programs in C and Python for deep learning (neural networks) used in the Training Phase, where the mathematical formulas are described in detail so that they can be easily compared with practical programs.作者: Keratectomy 時(shí)間: 2025-3-27 08:49 作者: Frenetic 時(shí)間: 2025-3-27 09:39
Flow Simulation with Deep Learningdynamics simulation, Sect.?. the formulation of the application of deep learning to fluid dynamics problems, Sect.?. recurrent neural networks that are suitable for the time-dependent problems covered in this chapter, and finally, Sect.?. a real application of deep learning to the fluid dynamics simulation.作者: Androgen 時(shí)間: 2025-3-27 16:28
1877-7341 lected there. Sample programs are included for the reader to try out in practice. This book is therefore useful for a wide range of readers interested in computational mechanics and deep learning..978-3-031-11849-4978-3-031-11847-0Series ISSN 1877-7341 Series E-ISSN 1877-735X 作者: frugal 時(shí)間: 2025-3-27 18:46
Mathematical Background for Deep Learningeural network including the error back propagation algorithm, Sect.?. the convolutional neural networks, which have become the mainstream of deep learning in recent years, and Sect.?. compares various methods for accelerating the training process. Finally, Sect.?. describes regularization methods to作者: novelty 時(shí)間: 2025-3-27 22:15 作者: Self-Help-Group 時(shí)間: 2025-3-28 05:29
Contact Mechanics with Deep Learningllision between objects is one of them. In this chapter, we study an application of deep learning to the contact search process, which is indispensable in contact and collision analysis. In particular, we focus on the contact between two smooth contact surfaces. In Sect.?., the basics of the contact作者: amenity 時(shí)間: 2025-3-28 09:53
Flow Simulation with Deep Learninguss the application of deep learning to fluid dynamics problems. Section?. describes the basic equations of fluid dynamics, Sect.?. the basics of the finite difference method, one of the most popular methods for solving fluid dynamics problems, Sect.?. a practical example of a two-dimensional fluid 作者: Stagger 時(shí)間: 2025-3-28 14:17 作者: Ischemia 時(shí)間: 2025-3-28 16:51
Bases for Computer Programmingmputational Mechanics with Deep Learning” from the perspective of programming. Section . describes some programs in the field of computational mechanics used in the Data Preparation Phase, including three topics discussed in the case study: the element stiffness matrix by using numerical quadrature 作者: DECRY 時(shí)間: 2025-3-28 20:12 作者: Mystic 時(shí)間: 2025-3-29 00:14 作者: profligate 時(shí)間: 2025-3-29 07:09
J. D. Sinclairregoing discussions, responses of the first sort have been considered, none of which - at least so I have suggested - have succeeded in proving the problems insubstantial. The time has now come to direct attention to the second sort of response, that is, to consider revisionist accounts of proportionalism.作者: CRATE 時(shí)間: 2025-3-29 10:46 作者: 吸引人的花招 時(shí)間: 2025-3-29 12:30
Book 2010offers molecular biologists a book in a progressive style where basic statistical methods are introduced and gradually elevated to an intermediate level, while providing statisticians knowledge of various biological data generated from the field of molecular biology, the types of questions of intere作者: Communal 時(shí)間: 2025-3-29 17:53 作者: ADJ 時(shí)間: 2025-3-29 22:54
Matthieu Salaf material from engineering and clinical sources ensures comThe book shows how the various paradigms of computational intelligence, employed either singly or in combination, can produce an effective structure for obtaining often vital information from ECG signals. The text is self-contained, address作者: 迎合 時(shí)間: 2025-3-30 02:43
Expanding Design Thinking with Methods from Futures Studies. Reflections on a Workshop with Chinese n which challenges are complex and ambiguous. Design thinking includes two distinct approaches: diverging and converging. It requires both a flexible way of understanding, to come with various ideas, and know-how to make informed decisions. These opposing activities are poured into an explanatory mo
