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Found 3 papers, 1 papers with code
Manifold Alignment-Based Multi-Fidelity Reduced-Order Modeling Applied to Structural Analysis
This work presents the application of a recently developed parametric, non-intrusive, and multi-fidelity reduced-order modeling method on high-dimensional displacement and stress fields arising from the structural analysis of geometries that differ in the size of discretization and structural topology. The proposed approach leverages manifold alignment to fuse inconsistent field outputs from high- and low-fidelity simulations by individually projecting their solution onto a common subspace.
A Fully Bayesian Gradient-Free Supervised Dimension Reduction Method using Gaussian Processes
The comparison shows that the proposed method improves the active subspace recovery and predictive accuracy, in both the deterministic and probabilistic sense, when only few model observations are available for training, at the cost of increased training time.
Application of Convolutional Neural Network to Predict Airfoil Lift Coefficient
The adaptability of the convolutional neural network (CNN) technique for aerodynamic meta-modeling tasks is probed in this work.
