David Bortz

Research interests:

The primary research in my group is on developing and analyzing computational methods for weak-form scientific machine learning. This class of methods is robust to substantial noise and highly computationally efficient. Modeling applications include system identification, governing equation discovery, coarse-graining, and reduced-order modeling.  Frequently, we apply these methods to biological systems, including collective and individual motion in wound healing, structured population dynamics, and disease ecology and epidemiology. However, more recently, we have expanded the applications to include atmospheric fluid models and computational plasma physics in the context of fusion. These projects have been financially supported by NSF, NIH-NIGMS, AFOSR, DOE, and CDPHE.