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<p>A growing challenge in research and industrial engineering applications is the need for repeated, systematic analysis of large-scale computational models, for example, patient-specific digital twins of diseased human organs: The analysis requires efficient implementation, data, resource management, and parallelization, possibly on distributed systems. To tackle these challenges and save many researchers from repetitive, time-consuming tasks, we present <i>QUEENS</i> (Quantification of Uncertain Effects in Engineering Systems), an open-source Python framework for composing and managing simulation analyses with arbitrary (physics-based) solvers on distributed computing infrastructures. Besides simulation management capabilities, <i>QUEENS</i> offers a comprehensive collection of efficiently implemented state-of-the-art algorithms ranging from routines for convergence studies and common optimization algorithms to more advanced sampling algorithms for uncertainty quantification and Bayesian inverse analysis. Additionally, we provide our latest cutting-edge research in multi-fidelity uncertainty quantification, efficient multi-fidelity Bayesian inverse analysis, and probabilistic machine learning. <i>QUEENS</i> adopts a Bayesian, probabilistic mindset but equally supports standard deterministic analysis without requiring prior knowledge of probability theory. The modular architecture allows rapid switching between common types of analyses and facilitates building sophisticated hierarchical algorithms. Encouraging natural incremental steps and scaling towards complexity allows researchers to consider the big picture while building towards it through smaller, manageable steps. We aim to create a large scientific community that seeks to collect, extend, and develop methods at the intersection of physics-based simulations, data-driven machine-learning techniques, and sophisticated simulation analytics. The open-source repository is available at <a href="https://github.com/queens-py/queens">https://github.com/queens-py/queens</a>.</p>

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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

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年8月3日

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  • Jonas Biehler,
  • Jonas Nitzler,
  • Sebastian Brandstaeter,
  • Maximilian Dinkel,
  • Volker Gravemeier,
  • Lea J. Haeusel,
  • Gil Robalo Rei,
  • Harald Willmann,
  • Barbara Wirthl,
  • Wolfgang A. Wall

摘要

A growing challenge in research and industrial engineering applications is the need for repeated, systematic analysis of large-scale computational models, for example, patient-specific digital twins of diseased human organs: The analysis requires efficient implementation, data, resource management, and parallelization, possibly on distributed systems. To tackle these challenges and save many researchers from repetitive, time-consuming tasks, we present QUEENS (Quantification of Uncertain Effects in Engineering Systems), an open-source Python framework for composing and managing simulation analyses with arbitrary (physics-based) solvers on distributed computing infrastructures. Besides simulation management capabilities, QUEENS offers a comprehensive collection of efficiently implemented state-of-the-art algorithms ranging from routines for convergence studies and common optimization algorithms to more advanced sampling algorithms for uncertainty quantification and Bayesian inverse analysis. Additionally, we provide our latest cutting-edge research in multi-fidelity uncertainty quantification, efficient multi-fidelity Bayesian inverse analysis, and probabilistic machine learning. QUEENS adopts a Bayesian, probabilistic mindset but equally supports standard deterministic analysis without requiring prior knowledge of probability theory. The modular architecture allows rapid switching between common types of analyses and facilitates building sophisticated hierarchical algorithms. Encouraging natural incremental steps and scaling towards complexity allows researchers to consider the big picture while building towards it through smaller, manageable steps. We aim to create a large scientific community that seeks to collect, extend, and develop methods at the intersection of physics-based simulations, data-driven machine-learning techniques, and sophisticated simulation analytics. The open-source repository is available at https://github.com/queens-py/queens.