Batman stands for Bayesian Analysis Tool for Modelling and uncertAinty quaNtification. It is a Python module distributed under the open-source CECILL-B license (MIT/BSD compatible). batman seamlessly allows to do statistical analysis (sensitivity analysis, Uncertainty Quantification, moments) based on non-intrusive ensemble experiment using any computer solver. It relies on open source python packages dedicated to statistics (OpenTURNS and scikit-learn). Main features are: Design of Experiment (LHS, low discrepancy sequences, MC), Resample the parameter space based on the physic and the sample, Surrogate Models (Gaussian process, Polynomial Chaos, RBF, scikit-learn’s regressors), Optimization (Expected Improvement), Sensitivity/Uncertainty Analysis (SA, UA) and Uncertainty Quantification (UQ), Visualization in n-dimensions (HDR, Kiviat, PDF), POD for database optimization or data reduction, Automatically manage code computations in parallel.
Keywords for this software
References in zbMATH (referenced in 4 articles , 1 standard article )
Showing results 1 to 4 of 4.
- Isabelle Mirouze, Sophie Ricci: Smurf: System for Modelling with Uncertainty Reduction, and Forecasting (2021) not zbMATH
- Trucchia, A.; Egorova, V.; Pagnini, G.; Rochoux, M. C.: On the merits of sparse surrogates for global sensitivity analysis of multi-scale nonlinear problems: application to turbulence and fire-spotting model in wildland fire simulators (2019)
- Trucchia, A.; Mattei, M. R.; Luongo, V.; Frunzo, L.; Rochoux, M. C.: Surrogate-based uncertainty and sensitivity analysis for bacterial invasion in multi-species biofilm modeling (2019)
- Pamphile T. Roy; Sophie Ricci; Romain Dupuis; Robin Campet; Jean-Christophe Jouhaud; Cyril Fournier: BATMAN: Statistical analysis for expensive computer codes made easy (2018) not zbMATH