Argyris Honorary Lecture: Michael Ortiz

December 2, 2020, 4:00 p.m. (CET)

Model-Free Data-Driven Science: Cutting out the Middleman

Time: December 2, 2020, 4:00 p.m. (CET)
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Due to the current situation, the Argyris Honorary lecture will take place as a virtual talk via Webex:
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Once a year, we award an Argyris Visiting Professorship to a leading personality in the field of simulation technology. With this award, we honor internationally renowned scientists from Germany and abroad, who are outstanding representatives of their disciplines in the field of simulation technology. This year's award goes to Michael Ortiz. He will talk about

Model-Free Data-Driven Science: Cutting out the Middleman

We have developed 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 entirely 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, linear and nonlinear elasticity, dynamics and plasticity, including scattered data and stochastic behavior. 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 as a special case of Data-Driven solutions. 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 experimental material data mining and identification, material data generation through multiscale analysis and fast search and data structure algorithms as a form of ansatz-free learning.

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