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 Articles Related News September 2018 Zaid Sawlan successfully defended his PhD thesis 3 min read · Thu, Sep 27 2018 News On September 26th, 2018, Zaid Sawlan successfully defended his Ph.D. thesis entitled "Statistical analysis and Bayesian methods for fatigue life prediction and inverse problems in linear time-dependent PDEs with uncertainties." January 2017 On Jan. 26th, 2017, PhD Candidate Zaid Sawlan will present his Proposal Thesis Defense 1 min read · Thu, Jan 26 2017 News In this work, we employ statistical and Bayesian techniques to analyze mathematical forward models. The forward models usually arise from phenomenological and physical phenomena and are expressed through regression-based models or partial differential equations (PDEs) associated with uncertain parameters and input data. August 2016 Prof. Tempone and Zaid Sawlan visited Prof. Ivo Babuska (ICES, University of Texas at Austin, USA) 1 min read · Sat, Aug 20 2016 News Prof. Tempone and Zaid Sawlan visited Prof. Ivo Babuska in August 2016 to continue their ongoing collaboration on computational predictions for metallic fatigue processes.
Zaid Sawlan successfully defended his PhD thesis 3 min read · Thu, Sep 27 2018 News On September 26th, 2018, Zaid Sawlan successfully defended his Ph.D. thesis entitled "Statistical analysis and Bayesian methods for fatigue life prediction and inverse problems in linear time-dependent PDEs with uncertainties."
On Jan. 26th, 2017, PhD Candidate Zaid Sawlan will present his Proposal Thesis Defense 1 min read · Thu, Jan 26 2017 News In this work, we employ statistical and Bayesian techniques to analyze mathematical forward models. The forward models usually arise from phenomenological and physical phenomena and are expressed through regression-based models or partial differential equations (PDEs) associated with uncertain parameters and input data.
Prof. Tempone and Zaid Sawlan visited Prof. Ivo Babuska (ICES, University of Texas at Austin, USA) 1 min read · Sat, Aug 20 2016 News Prof. Tempone and Zaid Sawlan visited Prof. Ivo Babuska in August 2016 to continue their ongoing collaboration on computational predictions for metallic fatigue processes.
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