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UID:pretalx-juliacon-2025-J3HTVE@pretalx.com
DTSTART;TZID=EST:20250725T150000
DTEND;TZID=EST:20250725T153000
DESCRIPTION:Knowledge of the physical laws acting on a system is often inco
 mplete. These gaps in our knowledge are referred to as missing physics. Ne
 ural network based techniques\, post-processed with interpretable machine 
 learning techniques such as symbolic regression\, are one way to learn thi
 s missing physics. We propose an efficient data gathering technique which 
 aims to make both the fitting and post-processing of the neural network as
  precise as possible\, showcased through a bioreactor case study.
DTSTAMP:20260410T233732Z
LOCATION:Lawrence Room 104 - Function Room
SUMMARY:Experimental Design for Missing Physics - Arno Strouwen\, Sebastian
  Micluța-Câmpeanu
URL:https://pretalx.com/juliacon-2025/talk/J3HTVE/
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