• Corpus ID: 239049623

DeepBND: a Machine Learning approach to enhance Multiscale Solid Mechanics

@article{Rocha2021DeepBNDAM,
  title={DeepBND: a Machine Learning approach to enhance Multiscale Solid Mechanics},
  author={Felipe F. Rocha and Simone Deparis and Pablo Antolin and Annalisa Buffa},
  journal={ArXiv},
  year={2021},
  volume={abs/2110.11141}
}
Effective properties of materials with random heterogeneous structures are typically determined by homogenising the mechanical quantity of interest in a window of observation. The entire problem setting encompasses the solution of a local PDE and some averaging formula for the quantity of interest in such domain. There are relatively standard methods in the literature to completely determine the formulation except for two choices: i) the local domain itself and the ii) boundary conditions… 
Physics-Informed Machine Learning and Uncertainty Quantification for Mechanics of Heterogeneous Materials
In this work, a model based on the Physics-Informed Neural Networks (PINNs) for solving elastic deformation of heterogeneous solids and associated Uncertainty Quantification (UQ) is presented. For

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