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.

Abstract:

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.

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Team Legato aims at building intuitive and interactive platforms for computational mechanics problems which allow the users to interact with their models and hence gain insights into unconventional counter-intuitive phenomena.
To make such platforms possible, we develop robust parallel numerical methods for direct and statistical inverse problems for free interface problems in mechanics.
We concentrate on error-controlled models and associated discretization schemes for multi-scale and multi-field problems and have been focusing on applications in Computational Biomechanics and Computational Materials Science.
Examples include the prediction of equilibrium phases in nano-composites, simulating fracture in complex aerospace structures, modeling cutting in real-time for surgical simulation.
Our research approach is multi-disciplinary. Our work has been carried out in collaboration with Mathematicians, Computer Scientists, Materials Scientists and Surgeons.
We publish all our codes in the open-source community and have been, in particular, collaborating with major open-source projects such as FEniCS and SOFA. We have also released our own open-source platforms for advanced discretization techniques such as the extended finite element method, meshless methods, isogeometric boundary element and finite element analysis with geometry-independent field approximations.

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