From Data to Equations: Weak Form Methods for Discovering Models from Noisy Data

What if noisy data could reveal the governing equations behind complex physical and biological systems? This in-person, one day intensive course introduces modern approaches to data-driven model discovery, an increasingly important paradigm across domains such as biochemistry, epidemiology, ecology, atmospheric science, and plasma physics. In many of these fields, large observational or experimental datasets are available, but the underlying mathematical models remain unknown or only partially understood.
Why Attend:
-Learn modern, data‑driven approaches to equation discovery using weak‑form methods
-Gain hands‑on experience implementing WSINDy and solving inverse problems
-Apply robust techniques to noisy, sparse, or real‑world data across scientific domains
-Expand your research toolkit with practical MATLAB or Python–based exercises