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    <title language="eng">Efficient reconstruction of model parameters using Bayesian target-vector optimization</title>
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    <title language="eng">Physics-informed Bayesian optimization of expensive-to-evaluate black-box functions</title>
    <abstract language="eng">Abstract&#13;
 Bayesian optimization with Gaussian process surrogates is a popular approach for optimizing expensive-to-evaluate functions in terms of time, energy, or computational resources. Typically, a Gaussian process models a scalar objective derived from observed data. However, in many real-world applications, the objective is a combination of multiple outputs from physical experiments or simulations. Converting these multidimensional observations into a single scalar can lead to information loss, slowing convergence and yielding suboptimal results. To address this, we propose to use multi-output Gaussian processes to learn the full vector of observations directly, before mapping them to the scalar objective via an inexpensive analytical function. This physics-informed approach retains more information from the underlying physical processes, improving surrogate model accuracy. As a result, the approach accelerates optimization and produces better final designs compared to standard implementations.</abstract>
    <parentTitle language="eng">Mach. Learn. Sci. Technol.</parentTitle>
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    <title language="eng">Data publication for Physics-informed Bayesian optimization of expensive-to-evaluate black-box functions</title>
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    <title language="eng">Impact Study of Numerical Discretization Accuracy on Parameter Reconstructions and Model Parameter Distributions</title>
    <abstract language="eng">In optical nano metrology numerical models are used widely for parameter reconstructions. Using the Bayesian target vector optimization method we fit a finite element numerical model to a Grazing Incidence x-ray fluorescence data set in order to obtain the geometrical parameters of a nano structured line grating. Gaussian process, stochastic machine learning surrogate models, were trained during the reconstruction and afterwards sampled with a Markov chain Monte Carlo sampler to determine the distribution of the reconstructed model parameters. The numerical discretization parameters of the used finite element model impact the numerical discretization error of the forward model. We investigated the impact of the polynomial order of the finite element ansatz functions on the reconstructed parameters as well as on the model parameter distributions. We showed that such a convergence study allows to determine numerical parameters which allows for efficient and accurate reconstruction results.</abstract>
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    <title language="eng">Machine learning approach for full Bayesian parameter reconstruction</title>
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    <title language="eng">High Purcell enhancement in all-TMDC nanobeam resonator designs with active monolayers for nanolasers</title>
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    <title language="eng">Source code and simulation results: High Purcell enhancement in all-TMDC nanobeam resonator designs with active monolayers for nanolasers</title>
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