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Data-Driven Physics-Informed Neural Networks: A Digital Twin Perspective
This study explores the potential of physics-informed neural networks (PINNs) for the realization of digital twins (DT) from various perspectives.
Compact and Intuitive Airfoil Parameterization Method through Physics-aware Variational Autoencoder
However, the high-dimensional nature of airfoil representation causes the challenging problem known as the "curse of dimensionality".
Towards Reliable Uncertainty Quantification via Deep Ensembles in Multi-output Regression Task
This study aims to comprehensively investigate the deep ensemble approach, an approximate Bayesian inference, in the multi-output regression task for predicting the aerodynamic performance of a missile configuration.
Physics-aware Reduced-order Modeling of Transonic Flow via $β$-Variational Autoencoder
Autoencoder-based reduced-order modeling (ROM) has recently attracted significant attention, owing to its ability to capture underlying nonlinear features.
Inverse design optimization framework via a two-step deep learning approach: application to a wind turbine airfoil
The inverse approach is computationally efficient in aerodynamic design as the desired target performance distribution is prespecified.
