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New Software Package PyThia for approximation of computationally expensive models released


Originally developed for fast global sensitivity analysis and efficient parameter reconstruction for applications in nano-optical metrology, PyThia provides an all purpose non-intrusive Python package to approximate high dimensional functions. Based on general polynomial chaos approximation obtained via linear regression, PyThia generates functional surrogates by relying purely on training data pairs of function input and output values. These surrogates can be used in a Bayesian inference setup to obtain information about the complete problem posterior via fast MCMC sampling and even yield a global sensitivity analysis through Sobol indices without any computational overhead. The package is easy to install and only relies on standard scientific packages such as NumPy and SciPy.
Applications include, among others, modeling scatterometry and X-Ray fluorescense experiments for shape determination of lithography masks, blood cell characterization via cytometry measurements and sensitivity analysis for cardiac modelling and ECG data.


Access: For more information on the software, installation guides, tutorials and a documentation please visit the Opens external link in new windowPyThia Homepage or the Opens external link in new windowGitLab repository. A short overview of Pythia's key features can also be found Opens internal link in current windowhere.


Related publication: Nando Farchmin, Martin Hammerschmidt, Philipp-Immanuel Schneider, Matthias Wurm, Bernd Bodermann, Markus Bär, and Sebastian Heidenreich "Efficient Bayesian inversion for shape reconstruction of lithography masks", Journal of Micro/Nanolithography, MEMS, and MOEMS 19(2), 024001 (5 May 2020), DOI: Opens external link in new window10.1117/1.JMM.19.2.024001


Expert: Opens local program for sending emailNando Farchmin (8.43)