PUMA publications for /https://puma.ub.uni-stuttgart.de/PUMA RSS feed for /2024-03-29T17:01:54+01:00Transientes Verhalten von Drucksensoren in hydraulischen Komponenten eines Verbrennungsmotorshttps://puma.ub.uni-stuttgart.de/bibtex/2e84d4e46ce83fcde3d54188089d99cf9/puma_ifspuma_ifs2024-03-28T18:04:00+01:00FKFS IFS AP <span data-person-type="author" class="authorEditorList "><span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="T. A. Lenz." itemprop="url" href="/person/18d93f22aabca38aee2736bb86bbc77a0/author/0"><span itemprop="name">T. Lenz.</span></a></span>, </span> und <span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="M. Bargende." itemprop="url" href="/person/18d93f22aabca38aee2736bb86bbc77a0/author/1"><span itemprop="name">M. Bargende.</span></a></span></span>. </span><span class="additional-entrytype-information">(<em><span>2017<meta content="2017" itemprop="datePublished"/></span></em>)</span>Thu Mar 28 18:04:00 CET 2024Rüsselsheim - GermanyTransientes Verhalten von Drucksensoren in hydraulischen Komponenten eines Verbrennungsmotors2017FKFS IFS AP Grundprobleme der Philosophie in geschichtlicher Entwicklunghttps://puma.ub.uni-stuttgart.de/bibtex/2cea180f12d4dadc4c37a3fba08041407/droesslerdroessler2024-03-28T13:26:05+01:00einführung geschichte grundlagen konstanz lehrbuch philosophie uni <span data-person-type="author" class="authorEditorList "><span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Gottfried Gabriel" itemprop="url" href="/person/120b1eb614665644c18b4454fd246ec10/author/0"><span itemprop="name">G. Gabriel</span></a></span></span>. </span><span class="additional-entrytype-information"><em><span itemprop="publisher">utb, Brill, Schöningh</span>, </em>(<em><span>2024<meta content="2024" itemprop="datePublished"/></span></em>)</span>Thu Mar 28 13:26:05 CET 2024Grundprobleme der Philosophie in geschichtlicher Entwicklung2024einführung geschichte grundlagen konstanz lehrbuch philosophie uni Bootstrapping Guarantees: Stability and Performance Analysis for Dynamic Encrypted Controlhttps://puma.ub.uni-stuttgart.de/bibtex/2cf2179716f9efdab02f511ec6ab5b22b/sebastianschlorsebastianschlor2024-03-28T13:23:08+01:00exc2075 myown pn4 <span data-person-type="author" class="authorEditorList "><span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Sebastian Schlor" itemprop="url" href="/person/193cbe8ad925b8d16b312ad48802cb341/author/0"><span itemprop="name">S. Schlor</span></a></span>, </span> und <span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Frank Allgöwer" itemprop="url" href="/person/193cbe8ad925b8d16b312ad48802cb341/author/1"><span itemprop="name">F. Allgöwer</span></a></span></span>. </span><span class="additional-entrytype-information"><span itemtype="http://schema.org/PublicationIssue" itemscope="itemscope" itemprop="isPartOf"><em><span itemprop="journal">submitted to L-CSS, Preprint: arxiv:2403.18571</span>, </em> </span>(<em><span>2024<meta content="2024" itemprop="datePublished"/></span></em>)</span>Thu Mar 28 13:23:08 CET 2024submitted to L-CSS, Preprint: arxiv:2403.18571Bootstrapping Guarantees: Stability and Performance Analysis for Dynamic Encrypted Control2024exc2075 myown pn4 Einbindung von New Urban Agenda, SDGs und Pariser Klimaschutzabkommen in die kommunale und nationale Stadtentwicklung in Deutschlandhttps://puma.ub.uni-stuttgart.de/bibtex/298051026ec4842e64cfabdc6f3c5246b/intcdc_fp2intcdc_fp22024-03-28T13:03:18+01:00tmp <span data-person-type="author" class="authorEditorList "><span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Franziska Schreiber" itemprop="url" href="/person/165ac837c1ead2592d5409c996d4a2cd1/author/0"><span itemprop="name">F. Schreiber</span></a></span>, </span><span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Barbara Burkel" itemprop="url" href="/person/165ac837c1ead2592d5409c996d4a2cd1/author/1"><span itemprop="name">B. Burkel</span></a></span>, </span><span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Philipp Misselwitz" itemprop="url" href="/person/165ac837c1ead2592d5409c996d4a2cd1/author/2"><span itemprop="name">P. Misselwitz</span></a></span>, </span> und <span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Christoph Walther" itemprop="url" href="/person/165ac837c1ead2592d5409c996d4a2cd1/author/3"><span itemprop="name">C. Walther</span></a></span></span>. </span><span class="additional-entrytype-information">(<em><span>2020<meta content="2020" itemprop="datePublished"/></span></em>)</span>Thu Mar 28 13:03:18 CET 2024BBSR-Online-PublikationEinbindung von New Urban Agenda, SDGs und Pariser Klimaschutzabkommen in die kommunale und nationale Stadtentwicklung in Deutschland04/20222020tmp Multifidelity Gaussian Processes for Predicting Shear Viscosity over Wide Ranges of Liquid State Points Based on Molecular Dynamics Simulationshttps://puma.ub.uni-stuttgart.de/bibtex/2524384f046280dc2c7ef3a565d89f139/niels_hansenniels_hansen2024-03-28T12:50:14+01:00exc2075 myown peerreviewed pn3 pn3-8 <span data-person-type="author" class="authorEditorList "><span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Maximilian Fleck" itemprop="url" href="/person/1a0b83a36e196cfec8d4c72f24ab02935/author/0"><span itemprop="name">M. Fleck</span></a></span>, </span><span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Joachim Gross" itemprop="url" href="/person/1a0b83a36e196cfec8d4c72f24ab02935/author/1"><span itemprop="name">J. Gross</span></a></span>, </span> und <span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Niels Hansen" itemprop="url" href="/person/1a0b83a36e196cfec8d4c72f24ab02935/author/2"><span itemprop="name">N. Hansen</span></a></span></span>. </span><span class="additional-entrytype-information"><span itemtype="http://schema.org/PublicationIssue" itemscope="itemscope" itemprop="isPartOf"><em><span itemprop="journal">Industrial & Engineering Chemistry Research</span>, </em> <em><span itemtype="http://schema.org/PublicationVolume" itemscope="itemscope" itemprop="isPartOf"><span itemprop="volumeNumber">63 </span></span>(<span itemprop="issueNumber">8</span>):
<span itemprop="pagination">3755-3765</span></em> </span>(<em><span>2024<meta content="2024" itemprop="datePublished"/></span></em>)</span>Thu Mar 28 12:50:14 CET 2024Industrial \& Engineering Chemistry Research83755-3765Multifidelity Gaussian Processes for Predicting Shear Viscosity over Wide Ranges of Liquid State Points Based on Molecular Dynamics Simulations632024exc2075 myown peerreviewed pn3 pn3-8 A linear multifidelity model based on Gaussian process (GP) regression is proposed that uses shear viscosities from a few molecular dynamics simulations as well as a few experimental shear viscosities to enable a high-quality prediction of this transport property over a large range of thermodynamic state points. Transport properties, such as viscosity, determined from molecular simulations are sensitive to the underlying force field, which is often parametrized with emphasis on vapor–liquid coexisting properties, so that mean absolute deviations of 20\% or more are frequently observed. A linear multifidelity model based on GP regression allows for compensating for such quantitative deviations based on very limited experimental data. The requirement for the molecular simulation is to describe the univariate relationship between the dimensionless shear viscosity and the reduced residual entropy qualitatively over a wide range of reduced residual entropy. This allows for a high-fidelity prediction of the shear viscosity for regions not covered by the experimental training data. The reliability of this new approach is showcased for 14 substances with more than 6000 data points, whereby only about 5 data points per species were used to train the multifidelity model. The few data points for training (selected in a narrow range of temperatures and ambient pressure, if possible) were used to mimic situations where substances are characterized by few experimental measurements, only.