References of "Hale, Jack 50001928"
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See detailNumerical methods for fracture/cutting of heterogeneous materials
Sutula, Danas UL; Agathos, Konstantinos UL; Ziaei Rad, Vahid UL et al

Presentation (2016, December)

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See detailUncertainty quantification for soft tissue biomechanics
Hauseux, Paul UL; Hale, Jack UL; Bordas, Stéphane UL

Poster (2016, December)

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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 detailStochastic FE analysis of brain deformation with different hyper-elastic models
Hauseux, Paul UL; Hale, Jack UL; Bordas, Stéphane UL

Scientific Conference (2016, September)

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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)

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See detailPhase field approach to fracture: Towards the simulation of cutting soft tissues
Ziaei Rad, Vahid UL; Hale, Jack UL; Maurini, Corrado et al

Scientific Conference (2016, June 08)

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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.

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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)

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

Report (2016)

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See detailOrchestrating clinical simulations with FEniCS
Weir, Phil; Ellerweg, Roland; Hale, Jack UL

Scientific Conference (2016, May)

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See detailBayesian statistical inference on the material parameters of a hyperelastic body
Hale, Jack UL; Farrel, Patrick E.; Bordas, Stéphane UL

in Proceedings of the ACME-UK 2016 24th Conference on Computational Mechanics (2016, March 31)

We present a statistical method for recovering the material parameters of a heterogeneous hyperelastic body. Under the Bayesian methodology for statistical inverse problems, the posterior distribution ... [more ▼]

We present a statistical method for recovering the material parameters of a heterogeneous hyperelastic body. Under the Bayesian methodology for statistical inverse problems, the posterior distribution encodes the probability of the material parameters given the available displacement observations and can be calculated by combining prior knowledge with a finite element model of the likelihood. In this study we concentrate on a case study where the observations of the body are limited to the displacements on the surface of the domain. In this type of problem the Bayesian framework (in comparison with a classical PDE-constrained optimisation framework) can give not only a point estimate of the parameters but also quantify uncertainty on the parameter space induced by the limited observations and noisy measuring devices. There are significant computational and mathematical challenges when solving a Bayesian inference problem in the case that the parameter is a field (i.e. exists infinite-dimensional Banach space) and evaluating the likelihood involves the solution of a large-scale system of non-linear PDEs. To overcome these problems we use dolfin-adjoint to automatically derive adjoint and higher-order adjoint systems for efficient evaluation of gradients and Hessians, develop scalable maximum aposteriori estimates, and use efficient low-rank update methods to approximate posterior covariance matrices. [less ▲]

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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 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 detailUsing Bayesian inference to recover the material parameters of a heterogeneous hyperelastic body
Hale, Jack UL; Farrell, Patrick; Bordas, Stéphane UL

Scientific Conference (2016)

We present a method for calculating a Bayesian uncertainty estimate on the recovered material parameters of a heterogeneous geometrically non-linear hyperelastic body. We formulate the problem in the ... [more ▼]

We present a method for calculating a Bayesian uncertainty estimate on the recovered material parameters of a heterogeneous geometrically non-linear hyperelastic body. We formulate the problem in the Bayesian inference framework [1]; given noisy and sparse observations of a body, some prior knowledge on the parameters and a parameter-to-observable map the goal is to recover the posterior distribution of the parameters given the observations. In this work we primarily focus on the challenges of developing dimension-independent algorithms in the context of very large inverse problems (tens to hundreds of thousands of parameters). Critical to the success of the method is viewing the problem in the correct infinite- dimensional function space setting [2]. With this goal in mind, we show the use of automatic symbolic differentiation techniques to construct high-order adjoint models [3], scalable maximum a posteriori (MAP) estimators, and efficient low-rank update methods to calculate credible regions on the posterior distribution [4]. [less ▲]

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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: 155 (15 UL)
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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 ▲]

Detailed reference viewed: 211 (5 UL)