Karlstad Applied Analysis Seminar (KAAS)

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Future seminars:

Talk-139

When: 16:00-17:00, 8 May 2024
What: The Surprising Robustness and Computational Efficiency of Weak Form System Identification
Who: David Bortz, Department of Applied Mathematics, University of Colorado Boulder, USA.
Where: online:https://kau-se.zoom.us/j/61616693592
Abstract: Recent advances in data-driven modeling approaches have proven highly successful in a wide range of fields in science and engineering. In this talk, I will briefly discuss several ubiquitous challenges with the conventional model development / discretization / parameter inference / model revision loop that our methodology attempts to address. I will present our weak form methodology which has proven to have surprising performance and robustness properties. In particular, I will describe our equation learning (WSINDy) and parameter estimation (WENDy) algorithms.  Lastly, I will discuss applications to several benchmark problems, illustrating how our approach addresses several of the above issues and offers advantages in terms of computational efficiency, noise robustness, and modest data needs (in an online learning context).

Talk-140

When: T.B.D, 29 May 2024
What: Studying cell ecology with spatial cumulant models.
Who: Sara Hamis, Department of Information Technology, Uppsala Univeristy, Sweden.
Where: T.B.D and online:https://kau-se.zoom.us/j/61616693592
Abstract: Spatial cumulant models (SCMs) are spatially resolved population models, formulated by differential equations. SCMs approximate the dynamics of two summary statistics generated by spatio-temporal point processes (STPPs): first-order spatial cumulants (densities), and second-order spatial cumulants (spatial covariances).
In this talk, I’ll exemplify how SCMs can be used to predict and control STPP-generated population dynamics. With a worked example, I’ll demonstrate that (1) SCMs can capture STPP-generated density dynamics, even when mean-field population models (MFPMs) fail to do so, and (2) SCM-informed treatment strategies outperform MFPM-informed strategies in terms of inhibiting population growths. Overall, our work demonstrates that SCMs provide a new framework in which to study cell-to-cell interactions and treatments that take cell-to-cell interactions into account.