https://puma.ub.uni-stuttgart.de/user/michaelhaas/MachineLearning%20bayesianoptimizationPUMA RSS feed for /user/michaelhaas/MachineLearning%20bayesianoptimization2026-04-21T22:46:06+02:00https://puma.ub.uni-stuttgart.de/bibtex/235c7e2feecb203afc29ed828b23bc4ae/michaelhaasmichaelhaas2026-04-20T16:13:31+02:00Ablation BayesianOptimization GaussianProcesses Laser MachineLearning Optimization Welding myown <span data-person-type="author" class="authorEditorList "><span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Andreas Michalowski" itemprop="url" href="/person/14ea9784e293ac172887a2bd6105b0af2/author/0"><span itemprop="name">A. Michalowski</span></a></span>, </span><span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Alexander Ilin" itemprop="url" href="/person/14ea9784e293ac172887a2bd6105b0af2/author/1"><span itemprop="name">A. Ilin</span></a></span>, </span><span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Alexander Kroschel" itemprop="url" href="/person/14ea9784e293ac172887a2bd6105b0af2/author/2"><span itemprop="name">A. Kroschel</span></a></span>, </span><span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Stephanie Karg" itemprop="url" href="/person/14ea9784e293ac172887a2bd6105b0af2/author/3"><span itemprop="name">S. Karg</span></a></span>, </span><span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Peter Stritt" itemprop="url" href="/person/14ea9784e293ac172887a2bd6105b0af2/author/4"><span itemprop="name">P. Stritt</span></a></span>, </span><span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Adina Dais" itemprop="url" href="/person/14ea9784e293ac172887a2bd6105b0af2/author/5"><span itemprop="name">A. Dais</span></a></span>, </span><span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Sebastian Becker" itemprop="url" href="/person/14ea9784e293ac172887a2bd6105b0af2/author/6"><span itemprop="name">S. Becker</span></a></span>, </span><span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Gerhard Kunz" itemprop="url" href="/person/14ea9784e293ac172887a2bd6105b0af2/author/7"><span itemprop="name">G. Kunz</span></a></span>, </span><span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Steffen Sonntag" itemprop="url" href="/person/14ea9784e293ac172887a2bd6105b0af2/author/8"><span itemprop="name">S. Sonntag</span></a></span>, </span><span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Martin Lustfeld" itemprop="url" href="/person/14ea9784e293ac172887a2bd6105b0af2/author/9"><span itemprop="name">M. Lustfeld</span></a></span></span> and 5 other author(s). </span><span class="additional-entrytype-information">(<em><span>2023<meta content="2023" itemprop="datePublished"/></span></em>)</span>Mon Apr 20 16:13:31 CEST 2026Advanced laser processing and its optimization with machine learning2023Ablation BayesianOptimization GaussianProcesses Laser MachineLearning Optimization Welding myown The flexibility of new laser sources and process-monitoring enables new possibilities in laser-based production technology, for instance the combination of different laser processes with many adjustable parameters. The fusion of domain knowledge and probabilistic models in the form of hybrid models allows an efficient optimization of these processes with machine learning. This can be a key technology to realize self-learning laser-based universal machines in the future. The article discusses some examples where algorithm-based optimization, partly supported by hybrid models, can already greatly reduce the effort required to find suitable process parameters.https://puma.ub.uni-stuttgart.de/bibtex/28c14d621fac5e000d2c65e9a33485af6/michaelhaasmichaelhaas2026-04-20T16:09:36+02:00Ablation BayesianOptimization GaussianProcesses Laser MachineLearning Optimization Welding myown <span data-person-type="author" class="authorEditorList "><span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Andreas Michalowski" itemprop="url" href="/person/14ea9784e293ac172887a2bd6105b0af2/author/0"><span itemprop="name">A. Michalowski</span></a></span>, </span><span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Alexander Ilin" itemprop="url" href="/person/14ea9784e293ac172887a2bd6105b0af2/author/1"><span itemprop="name">A. Ilin</span></a></span>, </span><span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Alexander Kroschel" itemprop="url" href="/person/14ea9784e293ac172887a2bd6105b0af2/author/2"><span itemprop="name">A. Kroschel</span></a></span>, </span><span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Stephanie Karg" itemprop="url" href="/person/14ea9784e293ac172887a2bd6105b0af2/author/3"><span itemprop="name">S. Karg</span></a></span>, </span><span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Peter Stritt" itemprop="url" href="/person/14ea9784e293ac172887a2bd6105b0af2/author/4"><span itemprop="name">P. Stritt</span></a></span>, </span><span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Adina Dais" itemprop="url" href="/person/14ea9784e293ac172887a2bd6105b0af2/author/5"><span itemprop="name">A. Dais</span></a></span>, </span><span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Sebastian Becker" itemprop="url" href="/person/14ea9784e293ac172887a2bd6105b0af2/author/6"><span itemprop="name">S. Becker</span></a></span>, </span><span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Gerhard Kunz" itemprop="url" href="/person/14ea9784e293ac172887a2bd6105b0af2/author/7"><span itemprop="name">G. Kunz</span></a></span>, </span><span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Steffen Sonntag" itemprop="url" href="/person/14ea9784e293ac172887a2bd6105b0af2/author/8"><span itemprop="name">S. Sonntag</span></a></span>, </span><span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Martin Lustfeld" itemprop="url" href="/person/14ea9784e293ac172887a2bd6105b0af2/author/9"><span itemprop="name">M. Lustfeld</span></a></span></span> and 5 other author(s). </span><span class="additional-entrytype-information"><em> Laser Applications in Microelectronic and Optoelectronic Manufacturing (LAMOM) XXVIII, </em><em><span itemprop="publisher">SPIE</span>, </em>(<em><span>2023<meta content="2023" itemprop="datePublished"/></span></em>)</span>Mon Apr 20 16:09:36 CEST 2026Advanced laser processing and its optimization with machine learningLaser Applications in Microelectronic and Optoelectronic Manufacturing (LAMOM) XXVIII2023Ablation BayesianOptimization GaussianProcesses Laser MachineLearning Optimization Welding myown The flexibility of new laser sources and process-monitoring enables new possibilities in laser-based production technology, for instance the combination of different laser processes with many adjustable parameters. The fusion of domain knowledge and probabilistic models in the form of hybrid models allows an efficient optimization of these processes with machine learning. This can be a key technology to realize self-learning laser-based universal machines in the future. The article discusses some examples where algorithm-based optimization, partly supported by hybrid models, can already greatly reduce the effort required to find suitable process parameters.