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- BE (Engineering Science) 1st class Honors, University of Auckland
- ME (Engineering Science) Distinction, University of Auckland
- D. Phil (Physics), University of Oxford
Simon Tavener received B.E. and M.E. degrees in Engineering Science at the University of Auckland before completing a Ph.D. in Physics from the University of Oxford. He spent a year as a postdoc at the Institute of Mathematics and Its Applications at the University of Minnesota and then 13 years in the Mathematics Department at Penn State. Simon joined CSU in 2000 and served for eight years as the Chair of Mathematics. Tavener’s current research interests are largely focused on numerical techniques for the accurate computation of multiscale, multiphysics problems, most recently including issues arising due to the presence of uncertainty. Simon has recently enjoyed two visiting fellowships at the Oxford Center for Collaborative Applied Mathematics.
deal.II implementation of a weak Galerkin finite element solver for Darcy flow Lecture Notes in Computer Science.
Solving linear elasticity by renovated Bernardi-Raugel elements on simplicial meshes SIAM Journal on Numerical Analysis.
Lowest-order weak Galerkin finite element methods for linear elasticity on rectangular and brick meshes Journal of Scientific Computing, 2018.
Lowest-order weak Galerkin finite element method for Darcy flow on convex polygonal meshes SIAM Journal on Scientific Computing, 2018.
Inference-based assessment of parameter identifiability in nonlinear biological models Journal of The Royal Society Interface, 144, 2018.
A two-field finite element solver for poroelasticity on quadrilateral meshes Lecture Notes in Computer Science, 2018.
The lowest-order weak Galerkin finite element method for the Darcy equation on quadrilateral and hybrid meshes Journal of Computational Physics, 2018.
Multiscale Modeling and Uncertainty Quantification for Nuclear Fuel Performance Battelle Energy Alliance, LLC, Columbus, OH (United States), 2017.
The Formulation of Stochastic Inverse Problems and Solution by Random Sampling To be determined.
The power of evolutionary rescue is constrained by genetic load Evolutionary Applications, 2017.