5 papers · 1 filter
Efficient Bayesian Optimal Experimental Design for Expensive Computational Models over Finite Design Sets
Maximilian Dinkel, Dragos C. Ana, Benedikt Goderbauer +1
Bayesian calibration is a powerful framework for identifying parameters in complex and large-scale computational models. However, when there is insufficient or poorly suited data f…
A Black Box Variational Inference Scheme for Inverse Problems with Demanding Physics-Based Models
G. Robalo Rei, C. P. Schmidt, J. Nitzler +2
Bayesian methods are particularly effective for addressing inverse problems due to their ability to manage uncertainties inherent in the inference process. However, employing these…
QUEENS: An Open-Source Python Framework for Solver-Independent Analyses of Large-Scale Computational Models
Jonas Biehler, Jonas Nitzler, Sebastian Brandstaeter +7
A growing challenge in research and industrial engineering applications is the need for repeated, systematic analysis of large-scale computational models, for example, patient-spec…
Dynamic Learning Rate Decay for Stochastic Variational Inference
Maximilian Dinkel, Gil Robalo Rei, Wolfgang A. Wall
Like many optimization algorithms, Stochastic Variational Inference (SVI) is sensitive to the choice of the learning rate. If the learning rate is too small, the optimization proce…
Solving Bayesian Inverse Problems With Expensive Likelihoods Using Constrained Gaussian Processes and Active Learning
Maximilian Dinkel, Carolin M. Geitner, Gil Robalo Rei +2
Solving inverse problems using Bayesian methods can become prohibitively expensive when likelihood evaluations involve complex and large scale numerical models. A common approach t…