Zobrazeno 1 - 10
of 96
pro vyhledávání: '"Schillings, Claudia"'
This paper introduces and analyses a continuous optimization approach to solve optimal control problems involving ordinary differential equations (ODEs) and tracking type objectives. Our aim is to determine control or input functions, and potentially
Externí odkaz:
http://arxiv.org/abs/2405.05124
Autor:
Kaarnioja, Vesa, Schillings, Claudia
This paper contributes to the study of optimal experimental design for Bayesian inverse problems governed by partial differential equations (PDEs). We derive estimates for the parametric regularity of multivariate double integration problems over hig
Externí odkaz:
http://arxiv.org/abs/2405.03529
Sampling from probability densities is a common challenge in fields such as Uncertainty Quantification (UQ) and Generative Modelling (GM). In GM in particular, the use of reverse-time diffusion processes depending on the log-densities of Ornstein-Uhl
Externí odkaz:
http://arxiv.org/abs/2402.15285
Autor:
Hanu, Matei, Hesser, Jürgen, Kanschat, Guido, Moviglia, Javier, Schillings, Claudia, Stallkamp, Jan
This paper addresses the challenging task of guide wire navigation in cardiovascular interventions, focusing on the parameter estimation of a guide wire system using Ensemble Kalman Inversion (EKI) with a subsampling technique. The EKI uses an ensemb
Externí odkaz:
http://arxiv.org/abs/2312.06460
In these notes, we investigate the tail behaviour of the norm of subgaussian vectors in a Hilbert space. The subgaussian variance proxy is given as a trace class operator, allowing for a precise control of the moments along each dimension of the spac
Externí odkaz:
http://arxiv.org/abs/2306.11404
In recent years, optimization in the learned latent space of deep generative models has been successfully applied to black-box optimization problems such as drug design, image generation or neural architecture search. Existing models thereby leverage
Externí odkaz:
http://arxiv.org/abs/2306.06684
Publikováno v:
Published by IOP Publishing Ltd, Inverse Problems, Volume 39, Number 9, Year 2023
We consider the Ensemble Kalman Inversion which has been recently introduced as an efficient, gradient-free optimisation method to estimate unknown parameters in an inverse setting. In the case of large data sets, the Ensemble Kalman Inversion become
Externí odkaz:
http://arxiv.org/abs/2302.11323
We propose an approach based on function evaluations and Bayesian inference to extract higher-order differential information of objective functions {from a given ensemble of particles}. Pointwise evaluation $\{V(x^i)\}_i$ of some potential $V$ in an
Externí odkaz:
http://arxiv.org/abs/2209.15420
We study the application of a tailored quasi-Monte Carlo (QMC) method to a class of optimal control problems subject to parabolic partial differential equation (PDE) constraints under uncertainty: the state in our setting is the solution of a parabol
Externí odkaz:
http://arxiv.org/abs/2208.02767
We propose a general framework for machine learning based optimization under uncertainty. Our approach replaces the complex forward model by a surrogate, which is learned simultaneously in a one-shot sense when solving the optimal control problem. Ou
Externí odkaz:
http://arxiv.org/abs/2112.11126