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

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• Prof Michael Ortiz, California Institute of Technology
• Friday 19 February 2021, 16:00-17:00
• Zoom Meeting ID: 819 1682 8857.

If you have a question about this talk, please contact Hilde Hambro.

email hh463@cam.ac.uk for passcode

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.

This talk is part of the Engineering - Mechanics Colloquia Research Seminars series.

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