Georg Stadler

Professor of Mathematics · Courant Institute of Mathematical Sciences

solvers for large-scale PDE systemsuncertainty quantificationscientific computingscientific machine learningBayesian inverse problemsextreme event probability estimationPDE-constrained optimizationoptimization under uncertainty

简介

Georg Stadler is a Professor of Mathematics at NYU's Courant Institute. His research focuses on solvers for large-scale PDE systems, uncertainty quantification, and scientific computing and machine learning. He works on Bayesian inverse problems, extreme event probability estimation, PDE-constrained optimization, and optimization under uncertainty, with applications in climate, plasma physics, and computational earth science.

教育经历

Ph.D. (Dr.), Mathematics, University of Graz, Austria, 2004
M.S. (Mag.), Mathematics, University of Graz, Austria, 2001
M.S. (Mag.), Mathematics and Geometry Education, Graz University of Technology and University of Graz, Austria, 2001

代表成果

Robust multigrid techniques for augmented Lagrangian preconditioning of incompressible Stokes equations with extreme viscosity variations
Extreme event probability estimation using PDE-constrained optimization and large deviation theory, with application to tsunamis
Scalable and efficient algorithms for the propagation of uncertainty from data through inference to prediction for large-scale problems, with application to flow of the Antarctic ice sheet

数据校验于 9/6/2026数据来源

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