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    <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>
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    <title language="eng">Physics informed Bayesian optimization for inverse design of diffractive optical elements</title>
    <parentTitle language="eng">Proc. SPIE</parentTitle>
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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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