QUEENS (Quantification of Uncertain Effects in Engineering Systems) is a Python framework for solver-independent multi-query analyses of large-scale computational models.
Our mission is to provide a comprehensive computational platform for the design and execution of multi-faceted simulation analysis tasks involving data-driven to physics-based computational models. We aim for a high-level abstraction to facilitate rapid prototyping. The platform supports diverse methods, from convergence studies to advanced probabilistic analysis. QUEENS enables incremental algorithm development based on basic Python models to workflows involving sophisticated large-scale (e.g., finite element) simulations on HPC platforms. Robust implementations and automated simulation execution should free researchers to focus on their research objectives. We strive for sustainability through the open-source community.
QUEENS offers a large collection of cutting-edge algorithms for deterministic and probabilistic analyses such as:
Bayesian Inverse Analysis
Estimate unknown model inputs or parameters by incorporating prior information and updating it wi...
Uncertainty Quantification
Evaluate and express the uncertainty in (computational) model predictions based on limited knowle...
Sensitivity Analysis
Apportion the uncertainty in the output to different sources of uncertainty in the input. General...
Parameter Studies
Parameter studies involve systematically varying input parameters of a model to analyze their eff...
Parameter Identification
Parameter identification is the process of determining unknown model parameters by fitting the mo...
Surrogate Modeling
Surrogate modeling involves creating simplified models, often based on data-driven or statistical...
Institute for Computational Mechanics
