About Zaid Sawlan Zaid Sawlan Postdoctoral Research Fellow, Stochastic Numerics Research Group Zaid Sawlan is a Postdoctoral Fellow at Professor Raul F. Tempone's Stochastic Numerics Research Group (STOCHNUM) at King Abdullah University of Science and Technology (KAUST). Prior to this, Zaid obtained a Ph.D. degree and a Master's degree in Applied Mathematics and Computational Sciences (AMCS) under Professor Raul F. Tempone's supervision. Research Interests Zaid's research interests include Model calibration and Bayesian model comparison for fatigue data, Bayesian inference in linear parabolic PDEs with noisy boundary conditions, and History matching using ensemble Kalman filters Projects Related Projects 2014 Bayesian analysis of metallic fatigue data Tue, Apr 1 2014 - Mon, Feb 1 2016 Fatigue tests at different speeds (cycles per minute) all along a range of mean loads are performed to determine the fatigue strength of a certain kind of metallic specimen. Such experiments involve expensive destructive tests and are therefore carefully designed and applied. Bayesian inference for linear parabolic partial differential equations Sun, Jun 1 2014 - Thu, Jan 1 2015 Details We are currently developing hierarchical Bayesian techniques to infer the unknown coefficients in initial-boundary value problems (IBVPs) for linear parabolic partial differential equations. Finitely many noisy measurements of the solution field are made available, at a sequence of time points, in the interior of a domain of interest and for the boundaries. The main novelty of our approach to solving such class of inverse problems relies on the assumption that the boundary parameters are unknown and modeled by means of adequate probability distributions. Using the linearity of the
Bayesian analysis of metallic fatigue data Tue, Apr 1 2014 - Mon, Feb 1 2016 Fatigue tests at different speeds (cycles per minute) all along a range of mean loads are performed to determine the fatigue strength of a certain kind of metallic specimen. Such experiments involve expensive destructive tests and are therefore carefully designed and applied.
Bayesian inference for linear parabolic partial differential equations Sun, Jun 1 2014 - Thu, Jan 1 2015 Details We are currently developing hierarchical Bayesian techniques to infer the unknown coefficients in initial-boundary value problems (IBVPs) for linear parabolic partial differential equations. Finitely many noisy measurements of the solution field are made available, at a sequence of time points, in the interior of a domain of interest and for the boundaries. The main novelty of our approach to solving such class of inverse problems relies on the assumption that the boundary parameters are unknown and modeled by means of adequate probability distributions. Using the linearity of the
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