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Artificial intelligence (AI, e.g., deep learning) is increasingly used to assist high-stakes decisions in areas such as finance, medicine, or autonomous driving. Upcoming regulations will require that the principles by which such algorithms arrive at their predictions should be transparent. However, while numerous “explainable” AI (XAI) methods have been proposed, the field is lacking formal...

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Virtual experiments simulating real measurement processes by means of numerical models have become increasingly important in modern metrology and industrial applications. Combined with Monte Carlo methods, virtual experiments have also been proposed for the evaluation of measurement uncertainties for a corresponding real experiment. However, such a proceeding is not always in line with the current...

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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...

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A recent study of PTB 8.4 scientists together with theoretical physicists at TU Berlin just appeared in Physical Review Letters and uncovers a surprising similarity between the transition from ordered patterns to turbulence in an non-equilibrium active fluid with the well known Ising transition of two-dimensional magnets in equilibrium conditions.

Turbulent vortex structures emerging in bacterial...

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Supplement 1 to the GUM (GUM-S1) extends the GUM uncertainty framework to nonlinear functions and non-Gaussian distributions.  For this purpose, it employs a Monte Carlo method that yields a probability density function for the measurand. This Monte Carlo method has been successfully applied in numerous applications throughout metrology. However, considerable criticism has been raised against the...

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