{"id":11450,"date":"2025-08-26T09:27:52","date_gmt":"2025-08-26T00:27:52","guid":{"rendered":"https:\/\/www.ibs.re.kr\/bimag\/?post_type=tribe_events&p=11450"},"modified":"2025-08-26T09:35:57","modified_gmt":"2025-08-26T00:35:57","slug":"weak-form-sciml-in-the-life-sciences-the-weak-form-is-stronger-than-you-think-david-bortz","status":"publish","type":"tribe_events","link":"https:\/\/www.ibs.re.kr\/bimag\/event\/weak-form-sciml-in-the-life-sciences-the-weak-form-is-stronger-than-you-think-david-bortz\/","title":{"rendered":"Weak form SciML in the Life Sciences: The Weak Form is Stronger than you Think – David Bortz"},"content":{"rendered":"

Abstract<\/strong><\/p>\n

The creation and inference of mathematical models is central to modern scientific discovery in the life sciences. As more realism is demanded of models, however, the conventional framework of biology-guided model proposal, discretization, parameter estimation, and model refinement becomes unwieldy, expensive, and computationally daunting. Recent advances in Weak form-based Scientific Machine Learning (WSciML) allow for the creation and inference of interpretable models directly from data via advanced numerical functional analysis, computational statistics, and numerical linear algebra techniques. This class of methods completely bypasses the need for forward-solve numerical discretizations and yields both parsimonious mathematical models and efficient parameter estimates. These methods are orders of magnitude faster and more accurate than traditional approaches and far more robust to the high noise levels common to data in the biological sciences. The combination of these features in a single framework provides a compelling alternative to both traditional modeling approaches as well as modern black-box neural networks. In this talk, I will present our weak form approach, describing our equation learning (WSINDy) and parameter estimation (WENDy) algorithms. I will demonstrate these performance properties via applications to several canonical problems in structured population modeling, cell migration, and mathematical epidemiology.<\/p>\n","protected":false},"excerpt":{"rendered":"

Abstract The creation and inference of mathematical models is central to modern scientific discovery in the life sciences. As more realism is demanded of models, however, the conventional framework of … <\/p>\n

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