1 citations · 2 across the 7 of their papers we have counts for
7 papers
A Bayesian model calibration framework for stochastic compartmental models with both time-varying and time-invariant parameters
Brandon Robinson, Philippe Bisaillon, Jodi D. Edwards +4
We consider state and parameter estimation for compartmental models having both time-varying and time-invariant parameters. Though the described Bayesian computational framework is…
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…
Probabilistic Prediction of Coalescence Flutter Using Measurements: Application to the Flutter Margin Method
Sandip Chajjed, Mohammad Khalil, Dominique Poirel +2
Zimmerman and Weissenburger's flutter margin method is widely used to estimate the aeroelastic coalescence flutter speed. In contrast to aeroelastic decay rates, the flutter margin…
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…
Robust Bayesian state and parameter estimation framework for stochastic dynamical systems with combined time-varying and time-invariant parameters
Philippe Bisaillon, Brandon Robinson, Mohammad Khalil +3
We consider state and parameter estimation for a dynamical system having both time-varying and time-invariant parameters. It has been shown that the robustness of the Markov Chain…
Domain Decomposition of Stochastic PDEs: Development of Probabilistic Wirebasket-based Two-level Preconditioners
Ajit Desai, Mohammad Khalil, Chris L. Pettit +2
Realistic physical phenomena exhibit random fluctuations across many scales in the input and output processes. Models of these phenomena require stochastic PDEs. For three-dimensio…