1 citations · 2 across the 3 of their papers we have counts for
3 papers
Sparse Bayesian neural networks for regression: Tackling overfitting and computational challenges in uncertainty quantification
Nastaran Dabiran, Brandon Robinson, Rimple Sandhu +3
Neural networks (NNs) are primarily developed within the frequentist statistical framework. Nevertheless, frequentist NNs lack the capability to provide uncertainties in the predic…
Exploring hierarchical framework of nonlinear sparse Bayesian learning algorithm through numerical investigations
Nastaran Dabiran, Brandon Robinson, Rimple Sandhu +4
Sparse Bayesian learning (SBL) has been extensively utilized in data-driven modeling to combat the issue of overfitting. While SBL excels in linear-in-parameter models, its direct…
Encoding nonlinear and unsteady aerodynamics of limit cycle oscillations using nonlinear sparse Bayesian learning
Rimple Sandhu, Brandon Robinson, Mohammad Khalil +3
This paper investigates the applicability of a recently-proposed nonlinear sparse Bayesian learning (NSBL) algorithm to identify and estimate the complex aerodynamics of limit cycl…