IWR School 2019: “A Crash Course in Machine Learning with Applications in Natural- and Life Sciences (ML4Nature)”

IWR School 2019: “A Crash Course in Machine Learning with Applications in Natural- and Life Sciences (ML4Nature)”

We would like to bring to your attention our upcoming IWR School which will focus on machine
learning with applications from Natural Sciences and Life Sciences. The school will take place
from September 23 to 27, 2019, in Heidelberg, and addresses young researchers (PhD,
PostDoc, Master) from Natural and Life Sciences who want to learn more about machine
A background in Machine Learning is not required. Besides introducing the basic concepts we
teach selected topics in more depth, such as deep learning, metric learning, transfer learning,
Bayesian inverse problems, and causality. Experts from machine learning, Natural Science and
Life Science explain how these machine learning approaches are utilized to solve problems in
their respective fields of research.


Further information on the IWR School 2019 are available at:

Michael Ortiz “Model-Free Data-Driven Computing”

It was a pleasure to welcome Michael Ortiz from the California Institute of Technology to give a seminar on the topic of “Model-Free Data-Driven Computing” at the University of Luxembourg. You can watch the entire seminar below.


We develop a new computing paradigm, which we refer to as Data-Driven Computing, according to which calculations are carried out directly from experimental material data and pertinent kinematic constraints and conservation laws, such as compatibility and equilibrium, thus bypassing the empirical material modeling step of conventional computing altogether. Data-driven solvers seek to assign to each material point the state from a prespecified data set that is closest to satisfying the conservation laws. Equivalently, data-driven solvers aim to find the state satisfying the conservation laws that is closest to the data set. The resulting data-driven problem thus consists of the minimization of a distance function to the data set in phase space subject to constraints introduced by the conservation laws. We demonstrate the data-driven paradigm and investigate the performance of data-driven solvers by means of several examples of application, including statics and dynamics of nonlinear three-dimensional trusses, and linear and nonlinear elasticity. In these tests, the data-driven solvers exhibit good convergence properties both with respect to the number of data points and with regard to local data assignment, including noisy material data sets containing outliers. The variational structure of the data-driven problem also renders it amenable to analysis. We find that the classical solutions are recovered in the case of linear elasticity. We identify conditions for convergence of Data-Driven solutions corresponding to sequences of approximating material data sets. Specialization to constant material data set sequences in turn establishes an appropriate notion of relaxation. We find that relaxation within the Data-Driven framework is fundamentally different from the classical relaxation of energy functions. For instance, we show that in the Data-Driven framework the relaxation of a bistable material leads to effective material data sets that are not graphs. I will finish my presentation with highlights on work in progress, including closed-loop Data-Driven analysis and experiments, Data-Driven molecular dynamics, Data-Driven inelasticity and publicly-editable material data repositories and data management from a Data-Driven perspective.

Legato is #HighlyCited

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.

Link to highly cited webpage http://hcr.stateofinnovation.thomsonreuters.com/