QUEENS
A general purpose framework for Uncertainty Quantification, Physics-Informed Machine Learning, Bayesian Optimization, Inverse Problems and Simulation Analytics on distributed computer systems.
Our mission is to provide a comprehensive computational platform for the design and execution of multi-faceted engineering algorithms involving physics-based simulations using a high-level abstraction for rapid prototyping. The platform supports diverse methods, from grid evaluations to advanced probabilistic analysis algorithms, and is accessible to users of all backgrounds. QUEENS enables incremental algorithm development based on basic Python models to workflows involving sophisticated finite element simulations on HPC platforms. We offer robust implementations and automated job management, freeing researchers to focus on their research objectives.
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. Genera...
Institute for Computational Mechanics
