Strategies for employing sparse, noisy sensing in characterizing complex systems and environments

We develop and implement statistical and deterministic approaches for the real-time contextualization of sparse data streams within suitable physics-inspired mathematical settings, as a means for characterizing natural and engineered systems in order that behavioral prognoses can be made in a principled manner.

Strategies for employing sparse, noisy sensing in characterizing complex systems and environments

Physics-inspired math model employing POD data-enriched Fredholm integral equation