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Robustness and Validation of Model and Digital Twins Deployment

Volkova, Svitana; Stracuzzi, David J.; Shafer, Jenifer; Ray, Jaideep R.; Pullum, Laura

For digital twins (DTs) to become a central fixture in mission critical systems, a better understanding is required of potential modes of failure, quantification of uncertainty, and the ability to explain a model’s behavior. These aspects are particularly important as the performance of a digital twin will evolve during model development and deployment for real-world operations.

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TYPE Other Report YEAR 2021
DOIOSTI

Data-driven learning of nonlocal physics from high-fidelity synthetic data

Computer Methods in Applied Mechanics and Engineering

You, Huaiqian; Yu, Yue; Trask, Nathaniel A.; Gulian, Mamikon G.; D'Elia, Marta D.

A key challenge to nonlocal models is the analytical complexity of deriving them from first principles, and frequently their use is justified a posteriori. In this work we extract nonlocal models from data, circumventing these challenges and providing data-driven justification for the resulting model form. Extracting data-driven surrogates is a major challenge for machine learning (ML) approaches, due to nonlinearities and lack of convexity — it is particularly challenging to extract surrogates which are provably well-posed and numerically stable. Our scheme not only yields a convex optimization problem, but also allows extraction of nonlocal models whose kernels may be partially negative while maintaining well-posedness even in small-data regimes. To achieve this, based on established nonlocal theory, we embed in our algorithm sufficient conditions on the non-positive part of the kernel that guarantee well-posedness of the learnt operator. These conditions are imposed as inequality constraints to meet the requisite conditions of the nonlocal theory. We demonstrate this workflow for a range of applications, including reproduction of manufactured nonlocal kernels; numerical homogenization of Darcy flow associated with a heterogeneous periodic microstructure; nonlocal approximation to high-order local transport phenomena; and approximation of globally supported fractional diffusion operators by truncated kernels.

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TYPE Journal Article YEAR 2021
DOIOSTIScopus
Results 876–900 of 9,998
Results 876–900 of 9,998