References of "Beex, Lars 50000691"
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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)

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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)

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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: 143 (15 UL)
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See detailVirtual-power-based quasicontinuum methods for discrete dissipative materials
Beex, Lars UL

Scientific Conference (2016, June)

In this presentation, the formulation of the virtual-power-based QC framework will be outlined for an elastoplastic truss lattice. Subsequently, the framework is applied to an actual discrete material.

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See detailVirtual-power-based quasicontinuum methods for discrete dissipative materials
Beex, Lars UL; Bordas, Stéphane UL

Scientific Conference (2016, June)

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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)

Detailed reference viewed: 181 (28 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: 71 (8 UL)
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See detailLarge-deformation lattice model for dry-woven fabrics including contact
Magliulo, Marco UL; Beex, Lars UL; Zilian, Andreas UL et al

Speeches/Talks (2016)

Short Presentation on the Quasi-continuum method

Detailed reference viewed: 298 (33 UL)
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See detailBayesian inference for material parameter identification
Rappel, Hussein UL; Beex, Lars UL; Hale, Jack UL et al

Report (2016)

Detailed reference viewed: 145 (14 UL)
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See detailA comparison of two meso-models for dry-woven fabrics and their multiscale equivalents
Beex, Lars UL; Duflot, Marc; Adam, Laurent

Scientific Conference (2016, April)

In this presentation, an X-braced spring mesomodel will be compared to a mesomodel in which the diagonal springs are replaced by rotational springs. The results are signi cantly di fferent, but some ... [more ▼]

In this presentation, an X-braced spring mesomodel will be compared to a mesomodel in which the diagonal springs are replaced by rotational springs. The results are signi cantly di fferent, but some disadvantages of the use of rotational springs will also be mentioned. A substantial part of the presentation will furthermore be dedicated to the multiscale quasicontinuum method to upscale the mesomodels in order to achieve e fficient macroscale computations. macroscale computations [less ▲]

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See detailSolving extremely ill-posed structures
Beex, Lars UL; Duflot, Marc; Adam, Laurent

Scientific Conference (2016, April)

Detailed reference viewed: 79 (4 UL)
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See detailAn introduction to Bayesian inference for material parameter identification
Rappel, Hussein UL; Beex, Lars UL; Hale, Jack UL et al

Presentation (2016, February 04)

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See detailReducing non-linear PDEs using a reduced integration proper orthogonal decomposition method
Schenone, Elisa; Hale, Jack UL; Beex, Lars UL et al

Scientific Conference (2016)

Detailed reference viewed: 141 (14 UL)
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See detailDiscrete mechanical models and upscaling techniques for discrete materials
Beex, Lars UL; Bordas, Stéphane UL

Poster (2016)

Numerous natural and man-made materials are essentially discrete structures at the mesoscale or microscale (see Fig. 1). Discrete mechanical models can be formulated to capture typical mechanical ... [more ▼]

Numerous natural and man-made materials are essentially discrete structures at the mesoscale or microscale (see Fig. 1). Discrete mechanical models can be formulated to capture typical mechanical phenomena arising from this discreteness. Failure in these materials, which often starts with the fracture of an individual bond, can be predicted based on the small-scale mechanics with these models. For failure, but also for non-local mechanics, no phenomenological descriptions are required in these models. This makes them more predictive than constitutive material models for this type of materials. [less ▲]

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Peer Reviewed
See detailReduced order method combined with domain decomposition
Baroli, Davide UL; Bordas, Stéphane UL; Beex, Lars UL et al

Scientific Conference (2016)

The complexities and nonlinearity of the PDEs in biomechanics and the requirement for rapid solution pose significant challenges for the biomedical applications. For these reasons, different methods for ... [more ▼]

The complexities and nonlinearity of the PDEs in biomechanics and the requirement for rapid solution pose significant challenges for the biomedical applications. For these reasons, different methods for reducing the complexity and solving efficiently have been investigated in the last 15 years. At the state-ofart, due to spatial different behaviours and highly accurate simulation required, a decomposition of physical domain is deeply investigated in reduced basis element method approaches. In this talk, the main focus is devoted to present suitable reduction strategy which combines a domain decomposition approach and a proper interface management with a proper orthogonal decomposition. We provide numerical tests implemented in DOLFIN[4] using SLEPc [3] and PETSc [1, 2] that show a speed up in forward runtime model. [less ▲]

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See detailMulti-scale methods for fracture: model learning across scales, digital twinning and factors of safety
: primer on Bayesian Inference
Bordas, Stéphane UL; Hale, Jack UL; Beex, Lars UL et al

Speeches/Talks (2015)

Fracture and material instabilities originate at spatial scales much smaller than that of the structure of interest: delamination, debonding, fibre break- age, cell-wall buckling, are examples of nano ... [more ▼]

Fracture and material instabilities originate at spatial scales much smaller than that of the structure of interest: delamination, debonding, fibre break- age, cell-wall buckling, are examples of nano/micro or meso-scale mechanisms which can lead to global failure of the material and structure. Such mech- anisms cannot, for computational and practical reasons, be accounted at structural scale, so that acceleration methods are necessary. We review in this presentation recently proposed approaches to reduce the computational expense associated with multi-scale modelling of frac- ture. In light of two particular examples, we show connections between algebraic reduction (model order reduction and quasi-continuum methods) and homogenisation-based reduction. We open the discussion towards suitable approaches for machine-learning and Bayesian statistical based multi-scale model selection. Such approaches could fuel a digital-twin concept enabling models to learn from real-time data acquired during the life of the structure, accounting for “real” environmental conditions during predictions, and, eventually, moving beyond the era of factors of safety. [less ▲]

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See detailMulti-scale methods for fracture: model learning across scales, digital twinning and factors of safety
Bordas, Stéphane UL; Beex, Lars UL; Kerfriden, Pierre et al

Scientific Conference (2015, November 18)

Authors: S. P. A. Bordas, L. A. A. Beex, P. Kerfriden, D. A. Paladim, O. Goury, A. Akbari, H. Rappel  Multi-scale methods for fracture: model learning across scales, digital twinning and factors of safety ... [more ▼]

Authors: S. P. A. Bordas, L. A. A. Beex, P. Kerfriden, D. A. Paladim, O. Goury, A. Akbari, H. Rappel  Multi-scale methods for fracture: model learning across scales, digital twinning and factors of safety Fracture and material instabilities originate at spatial scales much smaller than that of the structure of interest: delamination, debonding, fibre breakage, cell-wall buckling, are examples of nano/micro or meso-scale mechanisms which can lead to global failure of the material and structure. Such mechanisms cannot, for computational and practical reasons, be accounted at structural scale, so that acceleration methods are necessary.  We review in this presentation recently proposed approaches to reduce the computational expense associated with multi-scale modelling of fracture. In light of two particular examples, we show connections between algebraic reduction (model order reduction and quasi-continuum methods) and homogenisation-based reduction. We open the discussion towards suitable approaches for machine-learning and Bayesian statistical based multi-scale model selection. Such approaches could fuel a digital-twin concept enabling models to learn from real-time data acquired during the life of the structure, accounting for “real” environmental conditions during predictions, and, eventually, moving beyond the “factors of safety” era. [less ▲]

Detailed reference viewed: 394 (19 UL)