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A physics-informed operator regression framework for extracting data-driven continuum models
- Publication Year :
- 2020
-
Abstract
- The application of deep learning toward discovery of data-driven models requires careful application of inductive biases to obtain a description of physics which is both accurate and robust. We present here a framework for discovering continuum models from high fidelity molecular simulation data. Our approach applies a neural network parameterization of governing physics in modal space, allowing a characterization of differential operators while providing structure which may be used to impose biases related to symmetry, isotropy, and conservation form. We demonstrate the effectiveness of our framework for a variety of physics, including local and nonlocal diffusion processes and single and multiphase flows. For the flow physics we demonstrate this approach leads to a learned operator that generalizes to system characteristics not included in the training sets, such as variable particle sizes, densities, and concentration.<br />Comment: 37 pages, 15 figures
Details
- Database :
- arXiv
- Publication Type :
- Report
- Accession number :
- edsarx.2009.11992
- Document Type :
- Working Paper
- Full Text :
- https://doi.org/10.1016/j.cma.2020.113500