{"id":7100,"date":"2024-03-01T11:08:02","date_gmt":"2024-03-01T17:08:02","guid":{"rendered":"https:\/\/uwm.edu\/physics\/?post_type=tribe_events&p=7100"},"modified":"2024-03-12T12:18:12","modified_gmt":"2024-03-12T17:18:12","slug":"physics-colloquium-marcus-noack","status":"publish","type":"tribe_events","link":"https:\/\/uwm.edu\/physics\/event\/physics-colloquium-marcus-noack\/","title":{"rendered":"Physics Colloquium – Marcus Noack"},"content":{"rendered":"

Dr. Marcus Noack<\/strong>, Research Scientist, Lawrence Berkeley National Lab<\/p>\n

Next-Generation Gaussian Processes for Function Approximation, Uncertainty Quantification, and Decision-Making<\/em><\/p>\n

Gaussian processes (GPs) and Gaussian-related stochastic processes are powerful tools for function approximation, uncertainty quantification, global optimization, and autonomous data acquisition due to their robustness, analytical tractability, and natural inclusion of Bayesian uncertainty estimates. Even so, Gaussian processes are often criticized for poor approximation performance and neck-breaking computational costs in real-life applications. The reason for this gap, however, is not the methodology itself but rather a user-caused lack of flexibility and domain awareness of the underlying prior probability distribution. <\/p>\n

In this talk, I address many challenges of GPs that might inhibit optimal performance. We will discover hands-on solutions and tools that make implementation easy. The key takeaway for the audience will be a better understanding of Gaussian processes and ways to customize them for optimal performance.<\/p>\n

The event flyer is available here<\/a>.<\/p>\n","protected":false},"excerpt":{"rendered":"

Dr. Marcus Noack<\/strong>, Research Scientist, Lawrence Berkeley National Lab
\nNext-Generation Gaussian Processes for Function Approximation, Uncertainty Quantification, and Decision-Making<\/em>
\nGaussian processes (GPs) and Gaussian-related stochastic processes are powerful tools for function approximation, uncertainty quantification, global optimization, and autonomous data acquisition due to their robustness, analytical tractability, and natural inclusion of Bayesian uncertainty estimates. Even so, Gaussian processes are often criticized for poor approximation performance and neck-breaking computational costs in real-life applications. The reason for this gap, however, is not the methodology itself but rather a user-caused lack of flexibility and domain awareness of the underlying prior probability distribution. <\/p>\n","protected":false},"author":9647,"featured_media":0,"template":"","meta":{"_acf_changed":false,"_tribe_events_status":"","_tribe_events_status_reason":"","_tribe_events_is_hybrid":"","_tribe_events_is_virtual":"","_tribe_events_virtual_video_source":"","_tribe_events_virtual_embed_video":"","_tribe_events_virtual_linked_button_text":"","_tribe_events_virtual_linked_button":"","_tribe_events_virtual_show_embed_at":"","_tribe_events_virtual_show_embed_to":[],"_tribe_events_virtual_show_on_event":"","_tribe_events_virtual_show_on_views":"","_tribe_events_virtual_url":"","footnotes":"","uwm_wg_additional_authors":[]},"tags":[],"tribe_events_cat":[58],"class_list":["post-7100","tribe_events","type-tribe_events","status-publish","hentry","tribe_events_cat-physics-colloquia","cat_physics-colloquia"],"yoast_head":"\nPhysics & Astronomy<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/uwm.edu\/physics\/event\/physics-colloquium-marcus-noack\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Physics Colloquium - Marcus Noack\" \/>\n<meta property=\"og:description\" content=\"Dr. Marcus Noack, Research Scientist, Lawrence Berkeley National Lab Next-Generation Gaussian Processes for Function Approximation, Uncertainty Quantification, and Decision-Making Gaussian processes (GPs) and Gaussian-related stochastic processes are powerful tools for function approximation, uncertainty quantification, global optimization, and autonomous data acquisition due to their robustness, analytical tractability, and natural inclusion of Bayesian uncertainty estimates. 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