Skip to main content
Fachgebiete
Automobil + Motoren Bauwesen + Immobilien Business IT + Informatik Elektrotechnik + Elektronik Energie + Nachhaltigkeit Finance + Banking Management + Führung Marketing + Vertrieb Maschinenbau + Werkstoffe Versicherung + Risiko
DE
EN
Themenseiten
Organisationspsychologie Projektmanagement Marketing Smart Manufacturing
Bücher
Zeitschriften
Weitere Formate
Podcasts Webinare Technik Webinare Wirtschaft Kongresse Veranstaltungskalender Awards
Jetzt Einzelzugang starten
Zugang für Unternehmen
Referenzkunden
MTZ-Fachkongress

Antriebe und Energiesysteme von morgen


Austausch von Automobil- und Energiewirtschaft

Am 14./15. Mai geht es in Chemnitz um die Mobilitätswende durch Elektro- und Wasserstofffahrzeuge.
Erweiterte Suche
Anmelden
nach oben
Computational Mechanics
Erschienen in: Computational Mechanics 3/2023

15.11.2022 | Original Paper

Data-driven synchronization-avoiding algorithms in the explicit distributed structural analysis of soft tissue

verfasst von: Guoxiang Grayson Tong, Daniele E. Schiavazzi

Erschienen in: Computational Mechanics | Ausgabe 3/2023

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config
loading …

Abstract

We propose a data-driven framework to increase the computational efficiency of the explicit finite element method in the structural analysis of soft tissue. An encoder–decoder long short-term memory deep neural network is trained based on the data produced by an explicit, distributed finite element solver. We leverage this network to predict synchronized displacements at shared nodes, minimizing the amount of communication between processors. We perform extensive numerical experiments to quantify the accuracy and stability of the proposed synchronization-avoiding algorithm.
Vorheriger Artikel h-Adaptive radial basis function finite difference method for linear elasticity problems
Nächster Artikel Physics-informed machine learning for surrogate modeling of wind pressure and optimization of pressure sensor placement

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Jetzt informieren
Literatur
1.
Aslam M, Riaz O, Mumtaz S, Asif AD (2020) Performance comparison of GPU-based Jacobi solvers using CUDA provided synchronization methods. IEEE Access 8:31792–31812CrossRef
2.
Bakarji J, Champion K, Kutz JN, Brunton SL (2022) Discovering governing equations from partial measurements with deep delay autoencoders. arXiv preprint arXiv:​2201.​05136
3.
Bartezzaghi A, Cremonesi M, Parolini N, Perego U (2015) An explicit dynamics GPU structural solver for thin shell finite elements. Comput Struct 154:29–40CrossRef
4.
Belytschko T, Liu WK, Moran B, Elkhodary K (2014) Nonlinear finite elements for continua and structures. Wiley, HobokenMATH
5.
Bhat U, Munch SB (2022) Recurrent neural networks for partially observed dynamical systems. Phys Rev E 105:044205MathSciNetCrossRef
6.
Brunton SL, Proctor JL, Kutz JN (2016) Discovering governing equations from data by sparse identification of nonlinear dynamical systems. Proc Natl Acad Sci 113(15):3932–3937MathSciNetCrossRefMATH
7.
Chang B, Chen MM, Haber E, Chi EH (2019) AntisymmetricRNN: a dynamical system view on recurrent neural networks
8.
9.
Chen Z, Churchill V, Wu KL, Xiu DB (2022) Deep neural network modeling of unknown partial differential equations in nodal space. J Comput Phys 449:110782MathSciNetCrossRefMATH
10.
11.
Clough RW, Penzien J (1993) Dynamics of structures. Civil engineering series. McGraw-Hill, New YorkMATH
12.
Fu XH, Chang LB, Xiu DB (2020) Learning reduced systems via deep neural networks with memory. J Mach Learn Model Comput 1(2):97CrossRef
13.
Funahashi K, Nakamura Y (1993) Approximation of dynamical systems by continuous time recurrent neural networks. Neural Netw 6(6):801–806CrossRef
14.
Garnelo M, Rosenbaum D, Maddison C, Ramalho T, Saxton D, Shanahan M, Teh YW, Rezende D, Eslami SMA (Jul 2018) Conditional neural processes. In: Proceedings of the 35th international conference on machine learning, volume 80 of Proceedings of Machine Learning Research, pp 1704–1713. PMLR, 10–15
15.
Goodfellow I, Bengio Y, Courville A (2016) Deep learning. MIT Press, CambridgeMATH
16.
He KM, Zhang XY, Ren SQ, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp 770–778
17.
Hochreiter S, Schmidhuber J (1997) Long short-term memory. Neural Comput 9(8):1735–1780CrossRef
18.
Hu YH, Zhao T, Xú SX, Xu ZL, Lin LZ (2022) Neural-PDE: a RNN based neural network for solving time dependent PDEs. Commun Inf Syst
19.
Hughes TJR (2012) The finite element method: linear static and dynamic finite element analysis. Dover Publications, INC., Mineola
20.
21.
Joldes GR, Wittek A, Miller K (2010) Real-time nonlinear finite element computations on GPU-application to neurosurgical simulation. Comput Methods Appl Mech Eng 199(49–52):3305–3314CrossRefMATH
22.
23.
24.
Kaheman K, Kutz JN, Brunton SL (2020) SINDy-PI: a robust algorithm for parallel implicit sparse identification of nonlinear dynamics. Proc R Soc A Math Phys Eng Sci 476(2242):20200279MathSciNetMATH
25.
Kaiser E, Kutz JN, Brunton SL (2021) Data-driven discovery of Koopman Eigenfunctions for control. Mach Learn Sci Technol 2(3):035023CrossRef
26.
27.
Kim HJ, Vignon-Clementel IE, Coogan JS, Figueroa CA, Jansen KE, Taylor CA (2010) Patient-specific modeling of blood flow and pressure in human coronary arteries. Ann Biomed Eng 38(10):3195–3209CrossRef
28.
29.
Komatitsch D, Erlebacher G, Göddeke D, Michéa D (2010) High-order finite-element seismic wave propagation modeling with MPI on a large GPU cluster. J Comput Phys 229(20):7692–7714MathSciNetCrossRefMATH
30.
Kovacs A, Exl L, Kornell A, Fischbacher J, Hovorka M, Gusenbauer M, Breth L, Oezelt H, Yano M, Sakuma N, Kinoshita A, Shoji T, Kato A, Schrefl T (2022) Conditional physics informed neural networks. Commun Nonlinear Sci Numer Simul 104:106041MathSciNetCrossRefMATH
31.
Kronbichler M, Ljungkvist K (2019) Multigrid for matrix-free high-order finite element computations on graphics processors. ACM Trans Parallel Comput 6(1):1–32CrossRef
32.
33.
Kutz JN, Brunton SL, Brunton BW, Proctor JL (2016) Dynamic mode decomposition: data-driven modeling of complex systems. SIAM
34.
35.
Lu L, Jin PZ, Pang GF, Zhang ZQ, Karniadakis GE (2021) Learning nonlinear operators via deeponet based on the universal approximation theorem of operators. Nat Mach Intell 3(3):218–229CrossRef
36.
Malhotra P, Ramakrishnan A, Anand G, Vig L, Agarwal P, Shroff G (2016) LSTM-based encoder-decoder for multi-sensor anomaly detection
37.
McCaslin SE, Shiakolas PS, Dennis BH, Lawrence KL (2012) Closed-form stiffness matrices for higher order tetrahedral finite elements. Adv Eng Softw 44(1):75–79CrossRef
38.
Olovsson L, Simonsson K, Unosson M (2005) Selective mass scaling for explicit finite element analyses. Int J Numer Meth Eng 63(10):1436–1445CrossRefMATH
39.
Park SH, Kim BD, Kang CM, Chung CC, Choi JW (2018) Sequence-to-sequence prediction of vehicle trajectory via LSTM encoder-decoder architecture. In: 2018 IEEE intelligent vehicles symposium (IV). pp 1672–1678. IEEE
40.
Partin L, Geraci G, Rushdi A, Eldred MS, Schiavazzi DE (2022) Multifidelity data fusion in convolutional encoder/decoder networks
41.
Paszke A, Gross S, Massa F, Lerer A, Bradbury J, Chanan G, Killeen T, Lin ZM, Gimelshein N, Antiga L et al (2019) Pytorch: an imperative style, high-performance deep learning library. Adv Neural Inf Process Syst 32:8026–8037
42.
Qin T, Wu KL, Xiu DB (2019) Data driven governing equations approximation using deep neural networks. J Comput Phys 395:620–635MathSciNetCrossRefMATH
43.
Raissi M, Perdikaris P, Karniadakis GE (2019) Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations. J Comput Phys 378:686–707MathSciNetCrossRefMATH
44.
Sanchez-Gonzalez A, Godwin J, Pfaff T, Ying R, Leskovec J, Battaglia PW (2020) Learning to simulate complex physics with graph networks. In: Proceedings of the 37th international conference on machine learning. ICML’20. JMLR.org
45.
Schillinger D, Evans JA, Frischmann F, Hiemstra RR, Hsu MC, Hughes TJR (2015) A collocated C0 finite element method: Reduced quadrature perspective, cost comparison with standard finite elements, and explicit structural dynamics. Int J Numer Meth Eng 102(3–4):576–631CrossRefMATH
46.
Scovazzi G, Carnes B, Zeng X, Rossi S (2016) A simple, stable, and accurate linear tetrahedral finite element for transient, nearly, and fully incompressible solid dynamics: a dynamic variational multiscale approach. Int J Numer Meth Eng 106(10):799–839
47.
Seo J, Schiavazzi DE, Kahn AM, Marsden AL (2020) The effects of clinically-derived parametric data uncertainty in patient-specific coronary simulations with deformable walls. Int J Numer Methods Biomed Eng 36(8):e3351
48.
Seo J, Schiavazzi DE, Marsden AL (2019) Performance of preconditioned iterative linear solvers for cardiovascular simulations in rigid and deformable vessels. Comput Mech 64(3):717–739MathSciNetCrossRefMATH
49.
Shea DE, Brunton SL, Kutz JN (2021) SINDy-BVP: sparse identification of nonlinear dynamics for boundary value problems. Phys Rev Res 3:023255CrossRef
50.
Sherstinsky A (2020) Fundamentals of recurrent neural network (RNN) and long short-term memory (LSTM) network. Phys D 404:132306MathSciNetCrossRefMATH
51.
Shiakolas PS, Nambiar RV, Lawrence KL, Rogers WA (1992) Closed-form stiffness matrices for the linear strain and quadratic strain tetrahedron finite elements. Comput Struct 45(2):237–242CrossRefMATH
52.
Stoter SKF, Nguyen TH, Hiemstra RR, Schillinger D (2022) Variationally consistent mass scaling for explicit time-integration schemes of lower- and higher-order finite element methods
53.
Strbac V, Pierce DM, Vander Sloten J, Famaey N (2017) GPGPU-based explicit finite element computations for applications in biomechanics: the performance of material models, element technologies, and hardware generations. Comput Methods Biomech Biomed Engin 20(16):1643–1657CrossRef
54.
Taylor ZA, Cheng M, Ourselin S (2008) High-speed nonlinear finite element analysis for surgical simulation using graphics processing units. IEEE Trans Med Imaging 27(5):650–663CrossRef
55.
Vlachas PR, Byeon W, Wan ZY, Sapsis TP, Koumoutsakos P (2018) Data-driven forecasting of high-dimensional chaotic systems with long short-term memory networks. Proc R Soc A Math Phys Eng Sci 474(2213):20170844MathSciNetMATH
56.
57.
Zienkiewicz OC, Taylor RL, Too JM (1971) Reduced integration technique in general analysis of plates and shells. Int J Numer Meth Eng 3(2):275–290CrossRefMATH
Metadaten
Titel
Data-driven synchronization-avoiding algorithms in the explicit distributed structural analysis of soft tissue
verfasst von
Guoxiang Grayson Tong
Daniele E. Schiavazzi
Publikationsdatum
15.11.2022
Verlag
Springer Berlin Heidelberg
Erschienen in
Computational Mechanics / Ausgabe 3/2023
Print ISSN: 0178-7675
Elektronische ISSN: 1432-0924
DOI
https://doi.org/10.1007/s00466-022-02248-w

Weitere Artikel der Ausgabe 3/2023

Computational Mechanics 3/2023 Zur Ausgabe

Original Paper

A monolithic optimal control method for displacement tracking of Cosserat rod with application to reconstruction of C. elegans locomotion

Original Paper

h-Adaptive radial basis function finite difference method for linear elasticity problems

Original Paper

Discrepancies between Gaussian surface heat source model and ray tracing heat source model for numerical simulation of selective laser melting

Original Paper

A three-dimensional prediction method of stiffness properties of composites based on deep learning

Original Paper

A data-driven multi-flaw detection strategy based on deep learning and boundary element method

Original Paper

Memory repositioning in soil plasticity models used in contact problems

Neuer Inhalt

    Bildnachweise