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020 _a9783031366444
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082 0 4 _a006.3
_223
245 1 0 _aMachine Learning in Modeling and Simulation
_h[electronic resource] :
_bMethods and Applications /
_cedited by Timon Rabczuk, Klaus-Jürgen Bathe.
250 _a1st ed. 2023.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2023.
300 _aIX, 451 p. 150 illus., 135 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aComputational Methods in Engineering & the Sciences,
_x2662-4877
500 _aAcceso multiusuario
505 0 _aMachine Learning in Computer-Aided Engineering -- Artificial Neural Networks -- Gaussian Processes -- Machine Learning Methods for Constructing Dynamic Models from Data -- Physics-Informed Neural Networks: Theory and Applications -- Physics-Informed Deep Neural Operator Networks -- Digital Twin for Dynamical Systems -- Reduced Order Modeling -- Regression Models for Machine Learning -- Overview on Machine Learning Assisted Topology Optimization Methodologies -- Mixed-variable Concurrent Material, Geometry and Process Design in Integrated Computational Materials Engineering -- Machine Learning Interatomic Potentials: Keys to First-principles Multiscale Modeling.
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.
541 _fUABC ;
_cPerpetuidad
650 0 _aComputational intelligence.
650 0 _aMechanics, Applied.
650 0 _aDynamics.
650 0 _aNonlinear theories.
650 1 4 _aComputational Intelligence.
650 2 4 _aEngineering Mechanics.
650 2 4 _aApplied Dynamical Systems.
700 1 _aRabczuk, Timon.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
700 1 _aBathe, Klaus-Jürgen.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
710 2 _aSpringerLink (Online service)
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783031366437
776 0 8 _iPrinted edition:
_z9783031366451
776 0 8 _iPrinted edition:
_z9783031366468
830 0 _aComputational Methods in Engineering & the Sciences,
_x2662-4877
856 4 0 _zLibro electrónico
_uhttp://libcon.rec.uabc.mx:2048/login?url=https://doi.org/10.1007/978-3-031-36644-4
912 _aZDB-2-INR
912 _aZDB-2-SXIT
942 _cLIBRO_ELEC
999 _c262880
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