標(biāo)題: Titlebook: Machine Learning in Modeling and Simulation; Methods and Applicat Timon Rabczuk,Klaus-Jürgen Bathe Book 2023 The Editor(s) (if applicable) [打印本頁(yè)] 作者: Goiter 時(shí)間: 2025-3-21 18:10
書(shū)目名稱Machine Learning in Modeling and Simulation影響因子(影響力)
書(shū)目名稱Machine Learning in Modeling and Simulation影響因子(影響力)學(xué)科排名
書(shū)目名稱Machine Learning in Modeling and Simulation網(wǎng)絡(luò)公開(kāi)度
書(shū)目名稱Machine Learning in Modeling and Simulation網(wǎng)絡(luò)公開(kāi)度學(xué)科排名
書(shū)目名稱Machine Learning in Modeling and Simulation被引頻次
書(shū)目名稱Machine Learning in Modeling and Simulation被引頻次學(xué)科排名
書(shū)目名稱Machine Learning in Modeling and Simulation年度引用
書(shū)目名稱Machine Learning in Modeling and Simulation年度引用學(xué)科排名
書(shū)目名稱Machine Learning in Modeling and Simulation讀者反饋
書(shū)目名稱Machine Learning in Modeling and Simulation讀者反饋學(xué)科排名
作者: HUMID 時(shí)間: 2025-3-21 23:07
Reduced Order Modeling,odifications that are crucial in the applications are detailed. The progressive incorporation of machine learning methods is described, yielding first hybrid formulations and ending with pure data-driven approaches. An effort has been made to include references with applications of the methods being described.作者: apropos 時(shí)間: 2025-3-22 00:40 作者: 卵石 時(shí)間: 2025-3-22 05:36
Machine Learning Interatomic Potentials: Keys to First-Principles Multiscale Modeling, in the evaluation of materials and structural properties will be highlighted, and it will be shown that how MLIPs could efficiently address those issues. Last, the novel concept of MLIP-enabled first-principles multiscale modeling will be elaborated, and the practical prospect for the autonomous materials and structural design will be outlined.作者: 名詞 時(shí)間: 2025-3-22 11:10 作者: Epidural-Space 時(shí)間: 2025-3-22 13:43
Artificial Neural Networks,ted for addressing data-based engineering problems. This chapter will discuss the historical development of ANNs in the context of engineering usage; in that context, it will prove useful to divide the history into three main periods: pre-history, the first (MLP) age, and the second (deep) age.作者: 領(lǐng)先 時(shí)間: 2025-3-22 18:59 作者: 香料 時(shí)間: 2025-3-22 22:59
Physics-Informed Neural Networks: Theory and Applications,ng and training an artificial neural network model. These methods are applied in several numerical examples of forward and inverse problems, including the Poisson equation, Helmholtz equation, linear elasticity, and hyperelasticity.作者: 不真 時(shí)間: 2025-3-23 03:28
Book 2023gnition, credit card fraud detection, and spam filtering. There is clearly also the potential that ML techniques developed in Engineering and the Sciences will drastically increase the possibilities of analysis and accelerate the design to analysis time.? With the use of ML techniques, coupled to co作者: 斗爭(zhēng) 時(shí)間: 2025-3-23 07:42 作者: charisma 時(shí)間: 2025-3-23 10:33 作者: correspondent 時(shí)間: 2025-3-23 17:06 作者: ARK 時(shí)間: 2025-3-23 21:34 作者: 我正派 時(shí)間: 2025-3-24 01:30
T. J. Rogers,J. Mclean,E. J. Cross,K. Wordentional scientists.Also available online in springerLink.com..The present volume in the New Series of Landolt-B?rnstein provides critically evaluated data on phase diagrams, crystallographic and thermodynamic data of ternary alloy systems. Reliable phase diagrams provide materials scientists and engi作者: Bereavement 時(shí)間: 2025-3-24 05:14
J. Nathan Kutztional scientists.Also available online in springerLink.com..The present volume in the New Series of Landolt-B?rnstein provides critically evaluated data on phase diagrams, crystallographic and thermodynamic data of ternary alloy systems. Reliable phase diagrams provide materials scientists and engi作者: Amendment 時(shí)間: 2025-3-24 08:59
Cosmin Anitescu,Burak ?smail Ate?,Timon Rabczuktional scientists.Also available online in springerLink.com..The present volume in the New Series of Landolt-B?rnstein provides critically evaluated data on phase diagrams, crystallographic and thermodynamic data of ternary alloy systems. Reliable phase diagrams provide materials scientists and engi作者: abstemious 時(shí)間: 2025-3-24 12:40 作者: TAIN 時(shí)間: 2025-3-24 15:31
Tapas Tripura,Shailesh Garg,Souvik Chakrabortytional scientists.Also available online in springerLink.com..The present volume in the New Series of Landolt-B?rnstein provides critically evaluated data on phase diagrams, crystallographic and thermodynamic data of ternary alloy systems. Reliable phase diagrams provide materials scientists and engi作者: 吼叫 時(shí)間: 2025-3-24 19:23 作者: 怎樣才咆哮 時(shí)間: 2025-3-25 02:08 作者: 拱形大橋 時(shí)間: 2025-3-25 04:04
Ilias Chamatidis,Manos Stoumpos,George Kazakis,Nikos Ath. Kallioras,Savvas Triantafyllou,Vagelis Pleta of ternary alloy systems. Reliable phase diagrams provide materials scientists and engineers with basic information important for fundamental research, development and optimization of materials. ...The often conflicting literature data have been critically evaluated by Materials Science Internati作者: 可卡 時(shí)間: 2025-3-25 10:56
Tianyu Huang,Marisa Bisram,Yang Li,Hongyi Xu,Danielle Zeng,Xuming Su,Jian Cao,Wei Chentional scientists.Also available online in www.springerLink..The present volume in the New Series of Landolt-B?rnstein provides critically evaluated data on phase diagrams, crystallographic and thermodynamic data of ternary alloy systems. Reliable phase diagrams provide materials scientists and engi作者: 褪色 時(shí)間: 2025-3-25 15:10 作者: 胎兒 時(shí)間: 2025-3-25 18:31
Machine Learning in Computer Aided Engineering,s, improving or substituting many established approaches in Computer Aided Engineering (CAE), and also solving long-standing problems. In this chapter, we first review the ideas behind the most used ML approaches in CAE, and then discuss a variety of different applications which have been traditiona作者: Callus 時(shí)間: 2025-3-25 22:56 作者: 一窩小鳥(niǎo) 時(shí)間: 2025-3-26 03:43
Gaussian Processes,h not reaching the same widespread usage as neural network-based technology, it is also considered a key methodology for the machine learning pratictioner. In this short chapter, a basic introduction to the approach will be provided; following which, several extensions to the fundamental Gaussian pr作者: 飛來(lái)飛去真休 時(shí)間: 2025-3-26 07:25 作者: CHASE 時(shí)間: 2025-3-26 11:45
Physics-Informed Neural Networks: Theory and Applications,rks (PINNs) are among the earliest approaches, which attempt to employ the universal approximation property of artificial neural networks to represent the solution field. In this framework, solving the original differential equation can be seen as an optimization problem, where we seek to minimize t作者: Arboreal 時(shí)間: 2025-3-26 14:13
Physics-Informed Deep Neural Operator Networks,n an advection–diffusion reaction partial differential equation, or simply as a black box, e.g. a system-of-systems. The first neural operator was the Deep Operator Network (DeepONet) proposed in 2019 based on rigorous approximation theory. Since then, a few other less general operators have been pu作者: Offensive 時(shí)間: 2025-3-26 16:50
Digital Twin for Dynamical Systems,n this chapter. While physics-based models allow better generalization, a purely physics-based digital twin is often not robust because of noise in the data. On the other hand, gray-box modeling-based digital twin allows seamless fusion of data and physics. One of the primary challenges associated w作者: 學(xué)術(shù)討論會(huì) 時(shí)間: 2025-3-26 22:14
Reduced Order Modeling,ection with machine learning techniques. Although the presentation is applicable to many problems in science and engineering, the focus is first-order evolution problems in time and, more specifically, flow problems. Particular emphasis is put on the distinction between intrusive models, which make 作者: jovial 時(shí)間: 2025-3-27 01:12
Regression Models for Machine Learning,pectives. The non-Bayesian regression models, including the least square regression, ridge regression, and support vector regression, equipped or not equipped with kernel trick, are first examined as they share the same principle, which is to find an element in the parametrically indexed hypothesis 作者: 廚師 時(shí)間: 2025-3-27 07:49 作者: SLAG 時(shí)間: 2025-3-27 11:34 作者: Alcove 時(shí)間: 2025-3-27 16:54
Machine Learning Interatomic Potentials: Keys to First-Principles Multiscale Modeling,ation of diverse physical properties. MLIPs moreover offer extraordinary capabilities to conduct first-principles multiscale modeling, enabling the modeling of nanostructured materials at continuum level, with quantum mechanics level of accuracy and affordable computational costs. In this chapter, w作者: 看法等 時(shí)間: 2025-3-27 19:15 作者: CARE 時(shí)間: 2025-3-28 00:35 作者: Communal 時(shí)間: 2025-3-28 02:17
Regression Models for Machine Learning,p a unique learning skill, i.e. active learning, which aims at devising optimal design strategies for minimizing the number of simulator calls, especially when each call is computationally cumbersome. This is shown to be effective when applied to cutting-edge research on Bayesian numerical analysis 作者: CANON 時(shí)間: 2025-3-28 06:37 作者: ticlopidine 時(shí)間: 2025-3-28 12:07
Book 2023 and the Sciences in which the exciting aspects of machine learning are incorporated. The book is of value to any researcher and practitioner interested in research or applications of ML in the areas of scientific modeling and computer aided engineering..作者: Lament 時(shí)間: 2025-3-28 16:45 作者: 冒失 時(shí)間: 2025-3-28 22:01
K. Worden,G. Tsialiamanis,E. J. Cross,T. J. Rogersphases, pseudobinary systems, invariant equilibria, liquidus, solidus, and solvus surfaces, isothermal sections, temperature-composition sections, thermodynamics, materials properties and applications, and miscellanea. Finally, a detailed bibliography of all cited references is provided....In the pr作者: 我沒(méi)有命令 時(shí)間: 2025-3-29 01:29 作者: Ligneous 時(shí)間: 2025-3-29 03:18 作者: 強(qiáng)制令 時(shí)間: 2025-3-29 09:18 作者: 鉗子 時(shí)間: 2025-3-29 12:04 作者: 大猩猩 時(shí)間: 2025-3-29 18:30 作者: 單調(diào)女 時(shí)間: 2025-3-29 22:33
J. Nathan Kutz, isothermal sections, temperature-composition sections, thermodynamics, materials properties and applications, and miscellanea. Finally, a detailed bibliography of all cited references is provided....In the pr978-3-540-31694-7Series ISSN 1615-1844 Series E-ISSN 1616-9522 作者: Cabinet 時(shí)間: 2025-3-30 00:51 作者: 芳香一點(diǎn) 時(shí)間: 2025-3-30 04:38
