In today’s afternoon mail, from “Science at Thomson Reuters <Science@info.science.thomsonreuters.biz>”
Dear Stéphane PA Bordas,
I would like to extend congratulations on being named a 2016 Highly Cited Researcher and to announce the availability of the official 2016 list.
You were selected as a Highly Cited Researcher because your work has been identified as being among the most valuable and significant in the field. Very few researchers earn this distinction – writing the greatest number of reports, officially designated by Essential Science Indicators as Highly Cited Papers. In addition, these reports rank among the top 1% most cited works for their subject field and year of publication, earning them the mark of exceptional impact.
Now that you have achieved this designation, you will always retain your Highly Cited Researcher status.
It was great to have you in the Legato team and your work on composite delamination has been a pleasure to discuss with you. Excellent work! Have a safe trip back!
submitted to the International Journal for Numerical Methods in Bioengineering. This is collaborative work with @tomar.sk here at Legato (on ERC RealTCut), @phuoc who starts with us in a few weeks and was funded by my Strasbourg Institute of Advanced Studies Fellowship and the team of Stéphane Cotin (Inria MEMESIS) and Hadrien Courtecuisse (former post-doc now at ICube in Strasbourg). Congratulations to everyone. This paper shows is the result of a long-lasting collaboration between Mathematics, Computer Science and Engineering and shows that error estimators can be useful also in real-time simulations through an example in liver surgery.
In this paper we demonstrate the ability of a derivative-driven Monte Carlo estimator to accelerate the propagation of uncertainty through two high-level non-linear finite element models. The use of derivative information amounts to a correction to the standard Monte Carlo estimation procedure that reduces the variance under certain conditions. We express the finite element models in variational form using the high-level Unified Form Language (UFL). We derive the tangent linear model automatically from this high-level description and use it to efficiently calculate the required derivative information. To study the effectiveness of the derivative-driven method we consider two stochastic PDEs; a one- dimensional Burgers equation with stochastic viscosity and a three-dimensional geometrically non-linear Mooney-Rivlin hyperelastic equation with stochastic density and volumetric material parameter. Our results show that for these problems the first-order derivative-driven Monte Carlo method is around one order of magnitude faster than the standard Monte Carlo method and at the cost of only one extra tangent linear solution per estimation problem. We find similar trends when comparing with a modern non-intrusive multi-level polynomial chaos expansion method. We parallelise the task of the repeated forward model evaluations across a cluster using the ipyparallel and mpi4py software tools. A complete working example showing the solution of the stochastic viscous Burgers equation is included as supplementary material.
My long-standing colleague Chris Pearce (University of Glasgow) just passed on the sad news of the passing of Professor Nenad Bićanić.
I will remember Nenad as a passionate, gentle and dedicated mentor and I am deeply saddened by his passing. I am sure he inspired many young engineers in the many years he taught the subject and as many researchers to follow the path of knowledge.
I would like to wish all the best to his family in these difficult times.
Modelling interfacial cracking with non-matching cohesive interface elements
Computational Mechanics, 58(5), 731-746