國立虎尾科技大學 |

Advances in Peridynamics

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Advances in Peridynamics/ by Erdogan Madenci, Pranesh Roy, Deepak Behera.
作者:	Madenci, Erdogan.
其他作者:	Behera, Deepak.
面頁冊數:	XVI, 421 p. 240 illus., 234 illus. in color.online resource. :
Contained By:	Springer Nature eBook
標題:	Machine Learning. -
電子資源:	https://doi.org/10.1007/978-3-030-97858-7
ISBN:	9783030978587

Advances in Peridynamics
Madenci, Erdogan.

Advances in Peridynamics[electronic resource] /by Erdogan Madenci, Pranesh Roy, Deepak Behera. - 1st ed. 2022. - XVI, 421 p. 240 illus., 234 illus. in color.online resource.

Introduction -- Peridynamic Differential Operator -- Refinements In Bond-Based Peridynamics -- Refinements In Ordinary State-Based Peridynamics -- Weak Form Of Peridynamics -- Bond-Associated State-Based Peridynamics (Ba-Sb Pd) -- Ba-Sb Pd For Thermoelastic Deformation -- Ba-Sb Pd For Elastic- Plastic Deformation -- Ba-Sb Pd For Viscoleastic And Creep Deformation -- Ba-Sb Pd For Hyperelastic Deformation -- Ba-Sb Pd For Visco-Hyperelastic Deformation -- Ba-Sb Pd Modeling For Damage In Quasi-Brittle Materials -- Ba-Sb Pd Modeling For Impact Analysis -- Ba-Sb Pd Modeling Of Plates And Shells -- Ba-Sb Pd Modeling Under Axisymmetric Idealization -- Peridynamics For Multi-Scale Modeling -- Peridynamics For Machine Learning -- Peridynamics Coupled With Fem In Ansys Framework.

This book presents recent improvements in peridynamic modeling of structures. It provides sufficient theory and numerical implementation helpful to both new and existing researchers in the field. The main focus of the book is on the non-ordinary state-based (NOSB) peridynamics (PD) and its applications for performing finite deformation. It presents the framework for modeling high stretch polymers, viscoelastic materials, thermoelasticity, plasticity, and creep. It provides a systematic derivation for dimensionally reduced structures such as axisymmetric structures and beams. Also, it presents a novel approach to impose boundary conditions without suffering from displacement kinks near the boundary. Furthermore, it presents refinements to bond-based PD model by including rotation kinematics for modeling isotropic and composite materials. Moreover, it presents a PD – FEM coupling framework in ANSYS based on principle for virtual work. Lastly, it presents an application of neural networks in the peridynamic (PINN) framework. Sample codes are provided for readers to develop hands-on experience on peridynamic modeling. Describes new developments in peridynamics and their applications in the presence of material and geometric nonlinearity; Describes an approach to seamlessly couple PD with FE; Introduces the use of the neural network in the PD framework to solve engineering problems; Provides theory and numerical examples for researchers and students to self-study and apply in their research (Codes are provided as supplementary material); Provides theoretical development and numerical examples suitable for graduate courses.

ISBN: 9783030978587

Standard No.: 10.1007/978-3-030-97858-7doiSubjects--Topical Terms:

1137723
Machine Learning.

LC Class. No.: QC19.2-20.85

Dewey Class. No.: 530.15

Advances in Peridynamics
LDR:03782nam a22003975i 4500 001 1090190
003 DE-He213
005 20221022145018.0
007 cr nn 008mamaa
008 221228s2022 sz | s |||| 0|eng d
020 $a 9783030978587 $9 978-3-030-97858-7
024 7 $a 10.1007/978-3-030-97858-7 $2 doi
035 $a 978-3-030-97858-7
050 4 $a QC19.2-20.85
072 7 $a PHU $2 bicssc
072 7 $a SCI040000 $2 bisacsh
072 7 $a PHU $2 thema
082 0 4 $a 530.15 $2 23
100 1 $a Madenci, Erdogan. $4 aut $4 http://id.loc.gov/vocabulary/relators/aut $3 1021074
245 1 0 $a Advances in Peridynamics $h [electronic resource] / $c by Erdogan Madenci, Pranesh Roy, Deepak Behera.
250 $a 1st ed. 2022.
264 1 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2022.
300 $a XVI, 421 p. 240 illus., 234 illus. in color. $b online resource.
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
347 $a text file $b PDF $2 rda
505 0 $a Introduction -- Peridynamic Differential Operator -- Refinements In Bond-Based Peridynamics -- Refinements In Ordinary State-Based Peridynamics -- Weak Form Of Peridynamics -- Bond-Associated State-Based Peridynamics (Ba-Sb Pd) -- Ba-Sb Pd For Thermoelastic Deformation -- Ba-Sb Pd For Elastic- Plastic Deformation -- Ba-Sb Pd For Viscoleastic And Creep Deformation -- Ba-Sb Pd For Hyperelastic Deformation -- Ba-Sb Pd For Visco-Hyperelastic Deformation -- Ba-Sb Pd Modeling For Damage In Quasi-Brittle Materials -- Ba-Sb Pd Modeling For Impact Analysis -- Ba-Sb Pd Modeling Of Plates And Shells -- Ba-Sb Pd Modeling Under Axisymmetric Idealization -- Peridynamics For Multi-Scale Modeling -- Peridynamics For Machine Learning -- Peridynamics Coupled With Fem In Ansys Framework.
520 $a This book presents recent improvements in peridynamic modeling of structures. It provides sufficient theory and numerical implementation helpful to both new and existing researchers in the field. The main focus of the book is on the non-ordinary state-based (NOSB) peridynamics (PD) and its applications for performing finite deformation. It presents the framework for modeling high stretch polymers, viscoelastic materials, thermoelasticity, plasticity, and creep. It provides a systematic derivation for dimensionally reduced structures such as axisymmetric structures and beams. Also, it presents a novel approach to impose boundary conditions without suffering from displacement kinks near the boundary. Furthermore, it presents refinements to bond-based PD model by including rotation kinematics for modeling isotropic and composite materials. Moreover, it presents a PD – FEM coupling framework in ANSYS based on principle for virtual work. Lastly, it presents an application of neural networks in the peridynamic (PINN) framework. Sample codes are provided for readers to develop hands-on experience on peridynamic modeling. Describes new developments in peridynamics and their applications in the presence of material and geometric nonlinearity; Describes an approach to seamlessly couple PD with FE; Introduces the use of the neural network in the PD framework to solve engineering problems; Provides theory and numerical examples for researchers and students to self-study and apply in their research (Codes are provided as supplementary material); Provides theoretical development and numerical examples suitable for graduate courses.
650 2 4 $a Machine Learning. $3 1137723
650 2 4 $a Applied Dynamical Systems. $3 1366186
650 2 4 $a Engineering Mathematics. $3 1203947
650 2 4 $a Engineering Mechanics. $3 1366277
650 1 4 $a Mathematical Physics. $3 786661
650 0 $a Machine learning. $3 561253
650 0 $a Nonlinear theories. $3 527937
650 0 $a Dynamics. $3 592238
650 0 $a Engineering mathematics. $3 562757
650 0 $a Mechanics, Applied. $3 596630
650 0 $a Mathematical physics. $3 527831
700 1 $a Behera, Deepak. $e author. $4 aut $4 http://id.loc.gov/vocabulary/relators/aut $3 1397552
700 1 $a Roy, Pranesh. $e author. $4 aut $4 http://id.loc.gov/vocabulary/relators/aut $3 1397551
710 2 $a SpringerLink (Online service) $3 593884
773 0 $t Springer Nature eBook
776 0 8 $i Printed edition: $z 9783030978570
776 0 8 $i Printed edition: $z 9783030978594
776 0 8 $i Printed edition: $z 9783030978600
856 4 0 $u https://doi.org/10.1007/978-3-030-97858-7
912 $a ZDB-2-PHA
912 $a ZDB-2-SXP
950 $a Physics and Astronomy (SpringerNature-11651)
950 $a Physics and Astronomy (R0) (SpringerNature-43715)