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Found 6 papers, 2 papers with code
Digital Twins in Wind Energy: Emerging Technologies and Industry-Informed Future Directions
The contribution of this article lies in its synthesis of the current state of knowledge and its identification of future research needs and challenges from an industry perspective, ultimately providing a roadmap for future research and development in the field of digital twin and its applications in the wind energy industry.
Combining physics-based and data-driven techniques for reliable hybrid analysis and modeling using the corrective source term approach
In the current work, we demonstrate how a hybrid approach combining the best of PBM and DDM can result in models which can outperform them both.
Deep neural network enabled corrective source term approach to hybrid analysis and modeling
In this work, we introduce, justify and demonstrate the Corrective Source Term Approach (CoSTA) -- a novel approach to Hybrid Analysis and Modeling (HAM).
Hybrid analysis and modeling, eclecticism, and multifidelity computing toward digital twin revolution
Most modeling approaches lie in either of the two categories: physics-based or data-driven.
Physics guided machine learning using simplified theories
Recent applications of machine learning, in particular deep learning, motivate the need to address the generalizability of the statistical inference approaches in physical sciences.
A simple embedded discrete fracture-matrix model for a coupled flow and transport problem in porous media
We apply a recently developed embedded finite element method (EFEM) for the Darcy problem.
Numerical Analysis
