References of "Beex, Lars 50000691"
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See detailClassification of states and model order reduction of large scale Chemical Vapor Deposition processes with solution multiplicity
Koronaki, E.D.; Gkinis, P.A.; Beex, Lars UL et al

in Computers and Chemical Engineering (2018), 121

This paper presents an equation-free, data-driven approach for reduced order modeling of a Chemical Vapor Deposition (CVD) process. The proposed approach is based on process information provided by ... [more ▼]

This paper presents an equation-free, data-driven approach for reduced order modeling of a Chemical Vapor Deposition (CVD) process. The proposed approach is based on process information provided by detailed, high-fidelity models, but can also use spatio-temporal measurements. The Reduced Order Model (ROM) is built using the method-of-snapshots variant of the Proper Orthogonal Decomposition (POD) method and Artificial Neural Networks (ANN) for the identification of the time-dependent coefficients. The derivation of the model is completely equation-free as it circumvents the projection of the actual equations onto the POD basis. Prior to building the model, the Support Vector Machine (SVM) supervised classification algorithm is used in order to identify clusters of data corresponding to (physically) different states that may develop at the same operating conditions due to the inherent nonlinearity of the process. The different clusters are then used for ANN training and subsequent development of the ROM. The results indicate that the ROM is successful at predicting the dynamic behavior of the system in windows of operating parameters where steady states are not unique. [less ▲]

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See detailAn equation-free, nested, concurrent multiscale approach without scale-separation
Beex, Lars UL; Kerfriden, Pierre

Scientific Conference (2018, September)

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See detailMultiscale Modeling of Discrete Mesomodels for Dry-Woven Fabrics
Magliulo, Marco UL; Beex, Lars UL; Zilian, Andreas UL

Scientific Conference (2018, March)

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See detailAn adaptive variational Quasicontinuum methodology for lattice networks with localized damage
Rokos, Ondrej; Peerlings, Ron; Zeman, Jan et al

in International Journal for Numerical Methods in Engineering (2017), 112(2),

Lattice networks with dissipative interactions can be used to describe the mechanics of discrete meso‐structures of materials such as 3D‐printed structures and foams. This contribution deals with the ... [more ▼]

Lattice networks with dissipative interactions can be used to describe the mechanics of discrete meso‐structures of materials such as 3D‐printed structures and foams. This contribution deals with the crack initiation and propagation in such materials and focuses on an adaptive multiscale approach that captures the spatially evolving fracture. Lattice networks naturally incorporate non‐locality, large deformations and dissipative mechanisms taking place inside fracture zones. Because the physically relevant length scales are significantly larger than those of individual interactions, discrete models are computationally expensive. The Quasicontinuum (QC) method is a multiscale approach specifically constructed for discrete models. This method reduces the computational cost by fully resolving the underlying lattice only in regions of interest, while coarsening elsewhere. In this contribution, the (variational) QC is applied to damageable lattices for engineering‐scale predictions. To deal with the spatially evolving fracture zone, an adaptive scheme is proposed. Implications induced by the adaptive procedure are discussed from the energy‐consistency point of view, and theoretical considerations are demonstrated on two examples. The first one serves as a proof of concept, illustrates the consistency of the adaptive schemes and presents errors in energies. The second one demonstrates the performance of the adaptive QC scheme for a more complex problem. [less ▲]

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See detailMultiscale Modelling of Damage and Fracture in Discrete Materials Using a Variational Quasicontinuum Method
Rokos, Ondrej; Peerlings, Ron; Beex, Lars UL et al

Scientific Conference (2017, September 05)

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See detailBayesian inference to identify parameters in viscoelasticity
Rappel, Hussein UL; Beex, Lars UL; Bordas, Stéphane UL

in Mechanics of Time-Dependent Materials (2017)

This contribution discusses Bayesian inference (BI) as an approach to identify parameters in viscoelasticity. The aims are: (i) to show that the prior has a substantial influence for viscoelasticity, (ii ... [more ▼]

This contribution discusses Bayesian inference (BI) as an approach to identify parameters in viscoelasticity. The aims are: (i) to show that the prior has a substantial influence for viscoelasticity, (ii) to show that this influence decreases for an increasing number of measurements and (iii) to show how different types of experiments influence the identified parameters and their uncertainties. The standard linear solid model is the material description of interest and a relaxation test, a constant strain-rate test and a creep test are the tensile experiments focused on. The experimental data are artificially created, allowing us to make a one-to-one comparison between the input parameters and the identified parameter values. Besides dealing with the aforementioned issues, we believe that this contribution forms a comprehensible start for those interested in applying BI in viscoelasticity. [less ▲]

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See detaileXtended Variational Quasicontinuum Methodology for Modelling of Crack Propagation in Discrete Lattice Systems
Rokos, Ondrej; Peerlings, Ron; Zeman, Jan et al

Scientific Conference (2017, July 17)

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See detailAn equation-free multiscale method: a result of extending the quasicontinuum method to irregular structures
Beex, Lars UL; Kerfriden, Pierre

Scientific Conference (2017, July 16)

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See detailAn Enriched Quasi-Continuum Approach to Crack Propagation in Discrete Lattices
Rokos, Ondrej; Peerlings, Ron; Zeman, Jan et al

Scientific Conference (2017, June 14)

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See detailAn equation-free multiscale method applied to discrete networks
Beex, Lars UL; Kerfriden, Pierre

Scientific Conference (2017, June 06)

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See detailReduced basis Nitsche-based domain decomposition: a biomedical application
Baroli, Davide UL; Beex, Lars UL; Hale, Jack UL et al

Scientific Conference (2017, March 10)

Nowadays, the personalized biomedical simulations demand real-time efficient and reliable method to alleviate the computational complexity of high-fidelity simulation. In such applications, the necessity ... [more ▼]

Nowadays, the personalized biomedical simulations demand real-time efficient and reliable method to alleviate the computational complexity of high-fidelity simulation. In such applications, the necessity of solving different substructure, e.g. tissues or organs, with different numbers of the degrees of freedom and of coupling the reduced order spaces for each substructure poses a challenge in the on-fly simulation. In this talk, this challenge is taken into account employing the Nitsche-based domain decomposition technique inside the reduced order model [1]. This technique with respect to other domain decomposition approach allows obtaining a solution with the same accuracy of underlying finite element formulation and to flexibly treat interface with non-matching mesh. The robustness of the coupling is determined by the penalty coefficients that is chosen using ghost penalty technique [2]. Furthermore, to reduce the computational complexity of the on-fly assembling it is employed the empirical interpolation approach proposed in [3]. The numerical tests, performed using FEniCS[4], petsc4py and slepc4py [5], shows the good performance of the method and the reduction of computation cost. [1] Baroli, D., Beex L. and Bordas, S. Reduced basis Nitsche-based domain decomposition. In preparation. [2] Burman, E., Claus, S., Hansbo, P., Larson, M. G., & Massing, A. (2015). CutFEM: Discretizing geometry and partial differential equations. International Journal for Numerical Methods in Engineering, 104(7), 472-501. [3] E. Schenone, E., Beex,L., Hale, J.S., Bordas S. Proper Orthogonal Decomposition with reduced integration method. Application to nonlinear problems. In preparation. [4] A. Logg, K.-A. Mardal, G. N. Wells et al. Automated Solution of Differential Equations by the Finite Element Method, Springer 2012. [5] L. Dalcin, P. Kler, R. Paz, and A. Cosimo, Parallel Distributed Computing using Python, Advances in Water Resources, 34(9):1124-1139, 2011. http://dx.doi.org/10.1016/j.advwatres.2011.04.013 [less ▲]

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See detailA variational formulation of dissipative quasicontinuum methods
Rokos, Ondrej; Beex, Lars UL; Peerlings, Ron et al

in International Journal of Solids and Structures (2016), 102-103

Lattice systems and discrete networks with dissipative interactions are successfully employed as meso-scale models of heterogeneous solids. As the application scale generally is much larger than that of ... [more ▼]

Lattice systems and discrete networks with dissipative interactions are successfully employed as meso-scale models of heterogeneous solids. As the application scale generally is much larger than that of the discrete links, physically relevant simulations are computationally expensive. The QuasiContinuum (QC) method is a multiscale approach that reduces the computational cost of direct numerical simulations by fully resolving complex phenomena only in regions of interest while coarsening elsewhere. In previous work (Beex et al., J. Mech. Phys. Solids 64, 154-169, 2014), the originally conservative QC methodology was generalized to a virtual-power-based QC approach that includes local dissipative mechanisms. In this contribution, the virtual-power-based QC method is reformulated from a variational point of view, by employing the energy-based variational framework for rate-independent processes (Mielke and Roub cek, Rate-Independent Systems: Theory and Application, Springer-Verlag, 2015). By construction it is shown that the QC method with dissipative interactions can be expressed as a minimization problem of a properly built energy potential, providing solutions equivalent to those of the virtual-power-based QC formulation. The theoretical considerations are demonstrated on three simple examples. For them we verify energy consistency, quantify relative errors in energies, and discuss errors in internal variables obtained for different meshes and two summation rules. [less ▲]

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See detailBayesian inference for parameter identification in computational mechanics
Rappel, Hussein UL; Beex, Lars UL; Hale, Jack UL et al

Poster (2016, December 12)

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See detailBayesian inference for material parameter identification in elastoplasticity
Rappel, Hussein UL; Beex, Lars UL; Hale, Jack UL et al

Scientific Conference (2016, September 07)

Detailed reference viewed: 227 (29 UL)
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See detailPOD-based reduction methods, the Quasicontinuum Method and their Resemblance
Beex, Lars UL; Schenone, Elisa; Hale, Jack UL

Scientific Conference (2016, June 27)

Detailed reference viewed: 109 (11 UL)
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See detailA Bayesian approach for parameter identification in elastoplasticity
Rappel, Hussein UL; Beex, Lars UL; Hale, Jack UL et al

Scientific Conference (2016, June 09)

Detailed reference viewed: 131 (14 UL)
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See detailPOD-based Reduction Methods, the Quasicontinuum Method and their Resemblance
Schenone, Elisa; Hale, Jack UL; Beex, Lars UL

Scientific Conference (2016, June)

POD-based reduction methods and the quasicontinuum method share two similar reduction steps to increase the computational speed of large mechanical models. Here, they are compared with each other.

Detailed reference viewed: 64 (8 UL)