Georg Stadler

About

Georg Stadler is a Professor of Mathematics and Computer Science at the Courant Institute of New York University. His research encompasses a range of topics including sea ice strain rates, rheology parameter inference in mantle flow, and the optimization of coils for stellarators. His work has been published in notable journals such as the Journal of Computational Physics, Nature Geoscience, and Physics of Plasmas. Stadler is based at the Courant Institute in New York City, where he contributes to both research and teaching in his fields of expertise.

Research topics

• Computer Science
• Paleontology
• Oceanography
• Geology
• Computational physics
• Seismology
• Mathematics
• Geometry
• Mathematical analysis
• Physics

Selected publications

• Dynamics of the abrupt change in Pacific Plate motion around 50 million years ago

  Nature Geoscience · 2021 · 56 citations

  DOI

• Single-stage gradient-based stellarator coil design: Optimization for near-axis quasi-symmetry

  Journal of Computational Physics · 2022 · 32 citations

  DOI

• Constraining Earth’s nonlinear mantle viscosity using plate-boundary resolving global inversions

  Proceedings of the National Academy of Sciences · 2024-07-05 · 17 citations

  articleOpen accessSenior author

  Variable viscosity in Earth's mantle exerts a fundamental control on mantle convection and plate tectonics, yet rigorously constraining the underlying parameters has remained a challenge. Inverse methods have not been sufficiently robust to handle the severe viscosity gradients and nonlinearities (arising from dislocation creep and plastic failure) while simultaneously resolving the megathrust and bending slabs globally. Using global plate motions as constraints, we overcome these challenges by…

  PublisherDOI

• Large Deviation Theory-based Adaptive Importance Sampling for Rare Events in High Dimensions

  SIAM/ASA Journal on Uncertainty Quantification · 2023-07-11 · 12 citations

  articleSenior author

  .We propose a method for the accurate estimation of rare event or failure probabilities for expensive-to-evaluate numerical models in high dimensions. The proposed approach combines ideas from large deviation theory and adaptive importance sampling. The importance sampler uses a cross-entropy method to find an optimal Gaussian biasing distribution, and reuses all samples made throughout the process for both the target probability estimation and for updating the biasing distributions. Large devia…

  PublisherDOI

• Direct stellarator coil optimization for nested magnetic surfaces with precise quasi-symmetry

  Physics of Plasmas · 2023-04-01 · 11 citations

  articleOpen accessSenior author

  We present a robust optimization algorithm for the design of electromagnetic coils that generate vacuum magnetic fields with nested flux surfaces and precise quasi-symmetry. The method is based on a bilevel optimization problem, where the outer coil optimization is constrained by a set of inner least squares optimization problems whose solutions describe magnetic surfaces. The outer optimization objective targets coils that generate a field with nested magnetic surfaces and good quasi-symmetry.…

  PublisherOA PDFDOI

Recent grants

• Collaborative Research: Forward and inverse models of global plate motions and plate interactions

  NSF · $98k · 2017–2019

• Classification of Methods for Bayesian Inverse Problems Governed by Partial Differential Equations

  NSF · $180k · 2017–2021

• CDS&E: Collaborative Research: A Bayesian inference/prediction/control framework for optimal management of CO2 sequestration

  NSF · $140k · 2015–2017

Frequent coauthors

• Omar Ghattas

  The University of Texas at Austin

  68 shared

• Michael Gurnis

  California Institute of Technology

  41 shared

• Noémi Petra

  University of California, Merced

  38 shared

• Florian Wechsung

  31 shared

• Carsten Burstedde

  University of Bonn

  28 shared

• Antoine Cerfon

  New York University

  28 shared

• Andrew Giuliani

  Flatiron Institute

  28 shared

• Johann Rudi

  24 shared

Labs

• Georg Stadler's LabPI

  Research in applied and computational mathematics, Bayesian inverse problems, extreme events, scientific ML, optimization with PDEs, and parallel scientific computing.

Similar researchers at New York University

• Rob Fergush-95
• Dennis Shashah-84
• Mehryar Mohrih-77