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Machine Learning, Low-Rank Approximations and Reduced Order Modeling in Computational Mechanics

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ISBN: 9783039214099 / 9783039214105 Year: Pages: 254 DOI: 10.3390/books978-3-03921-410-5 Language: eng
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Technology (General) --- General and Civil Engineering
Added to DOAB on : 2019-12-09 11:49:15
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Abstract

The use of machine learning in mechanics is booming. Algorithms inspired by developments in the field of artificial intelligence today cover increasingly varied fields of application. This book illustrates recent results on coupling machine learning with computational mechanics, particularly for the construction of surrogate models or reduced order models. The articles contained in this compilation were presented at the EUROMECH Colloquium 597, « Reduced Order Modeling in Mechanics of Materials », held in Bad Herrenalb, Germany, from August 28th to August 31th 2018. In this book, Artificial Neural Networks are coupled to physics-based models. The tensor format of simulation data is exploited in surrogate models or for data pruning. Various reduced order models are proposed via machine learning strategies applied to simulation data. Since reduced order models have specific approximation errors, error estimators are also proposed in this book. The proposed numerical examples are very close to engineering problems. The reader would find this book to be a useful reference in identifying progress in machine learning and reduced order modeling for computational mechanics.

Keywords

parameter-dependent model --- surrogate modeling --- tensor-train decomposition --- gappy POD --- heterogeneous data --- elasto-viscoplasticity --- archive --- model reduction --- 3D reconstruction --- inverse problem plasticity --- data science --- model order reduction --- POD --- DEIM --- gappy POD --- GNAT --- ECSW --- empirical cubature --- hyper-reduction --- reduced integration domain --- computational homogenisation --- model order reduction (MOR) --- low-rank approximation --- proper generalised decomposition (PGD) --- PGD compression --- randomised SVD --- nonlinear material behaviour --- machine learning --- artificial neural networks --- computational homogenization --- nonlinear reduced order model --- elastoviscoplastic behavior --- nonlinear structural mechanics --- proper orthogonal decomposition --- empirical cubature method --- error indicator --- symplectic model order reduction --- proper symplectic decomposition (PSD) --- structure preservation of symplecticity --- Hamiltonian system --- reduced order modeling (ROM) --- proper orthogonal decomposition (POD) --- enhanced POD --- a priori enrichment --- modal analysis --- stabilization --- dynamic extrapolation --- computational homogenization --- large strain --- finite deformation --- geometric nonlinearity --- reduced basis --- reduced-order model --- sampling --- Hencky strain --- microstructure property linkage --- unsupervised machine learning --- supervised machine learning --- neural network --- snapshot proper orthogonal decomposition

Intelligent Control in Energy Systems

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ISBN: 9783039214150 / 9783039214167 Year: Pages: 508 DOI: 10.3390/books978-3-03921-416-7 Language: eng
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Science (General) --- Physics (General)
Added to DOAB on : 2019-12-09 11:49:15
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The editors of this Special Issue titled “Intelligent Control in Energy Systems” have attempted to create a book containing original technical articles addressing various elements of intelligent control in energy systems. In response to our call for papers, we received 60 submissions. Of those submissions, 27 were published and 33 were rejected. In this book, we offer the 27 accepted technical articles as well as one editorial. Authors from 15 countries (China, Netherlands, Spain, Tunisia, United Sates of America, Korea, Brazil, Egypt, Denmark, Indonesia, Oman, Canada, Algeria, Mexico, and the Czech Republic) elaborate on several aspects of intelligent control in energy systems. The book covers a broad range of topics including fuzzy PID in automotive fuel cell and MPPT tracking, neural networks for fuel cell control and dynamic optimization of energy management, adaptive control on power systems, hierarchical Petri Nets in microgrid management, model predictive control for electric vehicle battery and frequency regulation in HVAC systems, deep learning for power consumption forecasting, decision trees for wind systems, risk analysis for demand side management, finite state automata for HVAC control, robust ?-synthesis for microgrids, and neuro-fuzzy systems in energy storage.

Keywords

lithium-ion battery pack --- soft internal short circuit --- model-based fault detection --- battery safety --- internal short circuit resistance --- load frequency control --- model uncertainty --- ?-synthesis --- differential evolution --- decision tree --- preventive control --- Fault Ride Through Capability --- doubly-fed induction generator --- ancillary service --- frequency regulation --- demand response --- commercial/residential buildings --- HVAC systems --- model predictive control --- rule-based control --- position control --- static friction --- exhaust gas recirculation (EGR) valve system --- automotive application --- hybrid electric vehicle --- compound structured permanent-magnet motor --- energy management strategy --- instantaneous optimization minimum power loss --- back propagation (BP) neural network --- power transformer winding --- vibration characteristics --- multiphysical field analysis --- short-circuit experiment --- winding-fault characteristics --- occupancy model --- occupancy-based control --- model predictive control --- energy efficiency --- building climate control --- solar monitoring system --- photovoltaic array --- energy management --- demand side management --- operation limit violations --- probabilistic power flow --- network sensitivity --- neural networks --- railway --- high-speed railway --- neutral section --- medium voltage --- thyristor --- AC static switch --- adaptive backstepping --- nonlinear power systems --- sliding mode control --- error compensation --- ?-class function --- energy internet --- multi-energy complementary --- integrated energy systems --- distribution network planning --- electric power consumption --- multi-step forecasting --- long short term memory --- convolutional neural network --- system identification --- parameter estimation --- system modelling --- model reduction --- polynomial expansion --- orthogonal least square --- industrial process --- electric vehicle --- battery packs --- active balance --- model predictive control --- hierarchical Petri nets --- urban microgrids --- phase-load balancing --- fuzzy logic controller --- MPPT: maximum power point tracking --- photovoltaic system --- step-up boost converter --- proton exchange membrane fuel cell --- four phases interleaved boost converter --- neural network controller --- AC-DC converters --- bridgeless SEPIC PFC converter --- repetitive controller --- current distortion --- current controller design --- stochastic power system operating point drift --- wind integrated power system --- power oscillations --- adaptive damping control --- continuous voltage control --- multiple-point control --- interaction minimization --- pilot point --- adjacent areas --- ANFIS --- artificial neural network --- fuzzy --- small scale compressed air energy storage (SS-CAES) --- voltage controlling --- electric meter --- error estimation --- line loss --- RLS --- double forgetting factors --- hybrid power plant --- control architecture --- coordination of reserves --- frequency support --- frequency control dead band --- fast frequency response --- frequency containment reserve --- line switching --- voltage violations --- three-stage --- fractional order fuzzy PID controller --- neural network algorithm --- PEM fuel cell --- MPPT operation --- sensitivity analysis --- intelligent control --- artificial intelligence --- energy management system --- smart micro-grid --- energy systems --- intelligent buildings --- forecasting --- multi-agent control --- optimization

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MDPI - Multidisciplinary Digital Publishing Institute (2)


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eng (2)


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2019 (2)