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001482274 001__ 1482274
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001482274 019__ $$a1401961366
001482274 020__ $$a9783031366444$$q(eBook)
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001482274 0247_ $$a10.1007/978-3-031-36644-4$$2doi
001482274 035__ $$aSP(OCoLC)1402028815
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001482274 049__ $$aISEA
001482274 050_4 $$aQ325.5
001482274 08204 $$a006.3/1$$223/eng/20231011
001482274 24500 $$aMachine learning in modeling and simulation :$$bmethods and applications /$$cTimon Rabczuk, Klaus-Jürgen Bathe, editors.
001482274 264_1 $$aCham :$$bSpringer International Publishing AG,$$c2023.
001482274 300__ $$a1 online resource (ix, 451 pages) :$$billustrations (chiefly color)
001482274 336__ $$atext$$btxt$$2rdacontent
001482274 337__ $$acomputer$$bc$$2rdamedia
001482274 338__ $$aonline resource$$bcr$$2rdacarrier
001482274 4901_ $$aComputational Methods in Engineering and the Sciences Series
001482274 5050_ $$aIntro -- Preface -- Contents -- About the Editors -- 1 Machine Learning in Computer Aided Engineering -- 1.1 Introduction -- 1.2 Machine Learning Procedures Employed in CAE -- 1.2.1 Machine Learning Aspects and Classification of Procedures -- 1.2.2 Overview of Classical Machine Learning Procedures Used in CAE -- 1.3 Constraining to, and Incorporating Physics in, Data-Driven Methods -- 1.3.1 Incorporating Physics in, and Learning Physics From, the Dataset -- 1.3.2 Incorporating Physics in the Design of a ML Method -- 1.3.3 Data Assimilation and Correction Methods
001482274 5058_ $$a1.3.4 ML Methods Designed to Learn Physics -- 1.4 Applications of Machine Learning in Computer Aided Engineering -- 1.4.1 Constitutive Modeling and Multiscale Applications -- 1.4.2 Fluid Mechanics Applications -- 1.4.3 Structural Mechanics Applications -- 1.4.4 Machine Learning Approaches Motivated in Structural Mechanics and by Finite Element Concepts -- 1.4.5 Multiphysics Problems -- 1.4.6 Machine Learning in Manufacturing and Design -- 1.5 Conclusions -- References -- 2 Artificial Neural Networks -- 2.1 Introduction -- 2.2 Biological Motivation and Pre-history -- 2.2.1 Memory
001482274 5058_ $$a2.2.2 Learning -- 2.2.3 Parallel Distributed Processing Paradigm -- 2.2.4 The Artificial Neuron -- 2.2.5 The Perceptron -- 2.3 The First Age-The Multi-layer Perceptron -- 2.3.1 Existence of Solutions -- 2.3.2 Uniqueness of Solutions -- 2.3.3 Generalization and Regularization -- 2.3.4 Choice of Output Activations Functions -- 2.4 A First-Age Case Study: Structural Monitoring of an Aircraft Wing -- 2.5 The Second Age-Deep Learning -- 2.5.1 Convolutional Neural Networks (CNNs) -- 2.5.2 A Little More History -- 2.5.3 Other Recent Developments -- 2.6 Conclusions -- References -- 3 Gaussian Processes
001482274 5058_ $$a3.1 Introduction -- 3.1.1 A Visual Introduction To Gaussian Processes -- 3.1.2 Gaussian Process Regression -- 3.1.3 Implementation and Learning of the GP -- 3.2 Beyond the Gaussian Process -- 3.2.1 Large Training Data -- 3.2.2 Non-Gaussian Likelihoods -- 3.2.3 Multiple-Output GPs -- 3.3 A Case Study with Wind Turbine Power Curves -- 3.4 Conclusions -- References -- 4 Machine Learning Methods for Constructing Dynamic Models From Data -- 4.1 Introduction -- 4.2 Modeling Viewpoints -- 4.3 Learning Paradigms: Burgers' Equation -- 4.4 Dynamic Models From Data -- 4.4.1 Dynamic Mode Decomposition
001482274 5058_ $$a4.4.2 Sparse Identification of Nonlinear Dynamics -- 4.4.3 Neural Networks -- 4.5 Joint Discovery of Coordinates and Models -- 4.6 Conclusions -- References -- 5 Physics-Informed Neural Networks: Theory and Applications -- 5.1 Introduction -- 5.2 Basics of Artificial Neural Networks -- 5.2.1 Feed-Forward Neural Networks -- 5.2.2 Activation Functions -- 5.2.3 Training -- 5.2.4 Testing and Validation -- 5.2.5 Optimizers -- 5.3 Physics-Informed Neural Networks -- 5.3.1 Collocation Method -- 5.3.2 Energy Minimization Method -- 5.4 Numerical Applications -- 5.4.1 Forward Problems
001482274 506__ $$aAccess limited to authorized users.
001482274 520__ $$aIncludes bibliographical references.
001482274 520__ $$aMachine learning (ML) approaches have been extensively and successfully employed in various areas, like in economics, medical predictions, face recognition, 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 conventional methods like finite element and digital twin technologies, new avenues of modeling and simulation can be opened but the potential of these ML techniques needs to still be fully harvested, with the methods developed and enhanced. The objective of this book is to provide an overview of ML in Engineering and the Sciences presenting fundamental theoretical ingredients with a focus on the next generation of computer modeling in Engineering 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.
001482274 588__ $$aOnline resource; title from title screen (Ebook Central, viewed October 16, 2023).
001482274 650_6 $$aApprentissage automatique.
001482274 650_6 $$aIntelligence artificielle$$xApplications en ingénierie.
001482274 650_6 $$aSciences$$xInformatique.
001482274 650_6 $$aIngénierie$$xInformatique.
001482274 650_0 $$aMachine learning.$$vCongresses$$0(DLC)sh2008107143
001482274 650_0 $$aArtificial intelligence$$xEngineering applications.$$xMedical applications$$0(DLC)sh 88003000
001482274 650_0 $$aScience$$xData processing.
001482274 650_0 $$aEngineering$$xData processing.
001482274 655_0 $$aElectronic books.
001482274 7001_ $$aRabczuk, Timon.
001482274 7001_ $$aBathe, Klaus-Jürgen.
001482274 77608 $$iPrint version:$$tMachine learning in modeling and simulation.$$dCham : Springer International Publishing AG,c2023.$$z9783031366437
001482274 830_0 $$aComputational methods in engineering & the sciences.
001482274 852__ $$bebk
001482274 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-3-031-36644-4$$zOnline Access$$91397441.1
001482274 909CO $$ooai:library.usi.edu:1482274$$pGLOBAL_SET
001482274 980__ $$aBIB
001482274 980__ $$aEBOOK
001482274 982__ $$aEbook
001482274 983__ $$aOnline
001482274 994__ $$a92$$bISE