QUEENS software paper published in Engineering with Computers

3 August 2026

We are happy to announce the open-access publication of the software paper on QUEENS in Engineering with Computers.

QUEENS (Quantification of Uncertain Effects in Engineering Systems) is an open-source Python framework for solver-independent multi-query analyses of large-scale computational models, i.e., the repeated evaluation of variations of a particular computational model. QUEENS offers many types of analyses and scales naturally from entry-level studies to advanced probabilistic approaches: parameter studies, optimization, global sensitivity analysis, Bayesian inverse analysis, (probabilistic) surrogate models, and ultimately digital twins.

To make these analyses as easy as possible, QUEENS takes care of the tedious parts of multi-query studies — input file generation, job scheduling, remote execution on HPC clusters, and data management — so that researchers can focus on the analysis itself.

The paper gives an in-depth account of the framework: its modular three-layer architecture, the design principles, and an overview of the currently available methods — from grid-based sampling and global sensitivity analysis to Markov-chain Monte Carlo, Sequential Monte Carlo, variational inference, and Bayesian multi-fidelity methods. Two hands-on examples illustrate the setup in practice: a parameter study of a biomechanical tumor-growth model and the Bayesian calibration of a coupled fluid–structure interaction problem using a Gaussian process surrogate. From IMCS, Sebastian Brandstäter contributed as a shared first author.

IMCS is proud to be part of the QUEENS community, which develops the framework openly on GitHub. It is released under the LGPL and open to new contributors.

Reference:
Biehler, J., Nitzler, J., Brandstaeter, S. et al. QUEENS: an open-source Python framework for solver-independent analyses of large-scale computational models – from parameter studies and identification, sensitivity analysis, surrogates, optimization, (Bayesian) forward and backward uncertainty quantification to digital twinning. Engineering with Computers 42, 147 (2026). https://doi.org/10.1007/s00366-026-02337-x

Further resources:

• Project homepage: queens-py.org
• Source code: github.com/queens-py/queens