171 citations · 689 across the 32 of their papers we have counts for
6 papers · 1 filter
Single-stage gradient-based stellarator coil design: Optimization for near-axis quasi-symmetry
Andrew Giuliani, Florian Wechsung, Antoine Cerfon +2
We present a new coil design paradigm for magnetic confinement in stellarators. Our approach directly optimizes coil shapes and coil currents to produce a vacuum quasi-symmetric ma…
Extreme event probability estimation using PDE-constrained optimization and large deviation theory, with application to tsunamis
Shanyin Tong, Eric Vanden-Eijnden, Georg Stadler
We propose and compare methods for the analysis of extreme events in complex systems governed by PDEs that involve random parameters, in situations where we are interested in quant…
Optimal design of large-scale Bayesian linear inverse problems under reducible model uncertainty: good to know what you don't know
Alen Alexanderian, Noemi Petra, Georg Stadler +1
We consider optimal design of infinite-dimensional Bayesian linear inverse problems governed by partial differential equations that contain secondary reducible model uncertainties,…
Hierarchical Matrix Approximations of Hessians Arising in Inverse Problems Governed by PDEs
Ilona Ambartsumyan, Wajih Boukaram, Tan Bui-Thanh +5
Hessian operators arising in inverse problems governed by partial differential equations (PDEs) play a critical role in delivering efficient, dimension-independent convergence for…
Advanced Newton Methods for Geodynamical Models of Stokes Flow with Viscoplastic Rheologies
Johann Rudi, Yu-hsuan Shih, Georg Stadler
Strain localization and resulting plasticity and failure play an important role in the evolution of the lithosphere. These phenomena are commonly modeled by Stokes flows with visco…
Bayesian approach to inverse scattering with topological priors
Ana Carpio, Sergei Iakunin, Georg Stadler
We propose a Bayesian inference framework to estimate uncertainties in inverse scattering problems. Given the observed data, the forward model and their uncertainties, we find the…