We propose an approach to learn effective evolution equations for large systems of
interacting agents. This is demonstrated on two examples, a well-studied system of coupled …

Effective online modeling of papermaking wastewater treatment processes (WWTPs) is an
important means to ensure wastewater recycling and harmless discharge. A composite …

Systems of coupled dynamical units (eg, oscillators or neurons) are known to exhibit
complex, emergent behaviors that may be simplified through coarse-graining: a process in …

Fed-batch culture is an established operation mode for the production of biologics using
mammalian cell cultures. Quantitative modeling integrates both kinetics for some key …

Manifold-learning techniques are routinely used in mining complex spatiotemporal data to
extract useful, parsimonious data representations/parametrizations; these are, in turn, useful …

Large collections of coupled, heterogeneous agents can manifest complex dynamical
behavior presenting difficulties for simulation and analysis. However, if the collective …

Finding accurate reduced descriptions for large, complex, dynamically evolving networks is
a crucial enabler to their simulation, analysis, and ultimately design. Here, we propose and …

We investigate networks of globally coupled limit cycle oscillators. In small ensembles, we
discuss how symmetry-broken states, such as cluster and chimera states, bifurcate off the …

In this dissertation we develop a suite of data-driven modeling techniques for dynamical
systems by leveraging manifold learning, dimensionality reduction, and deep learning …

We discuss the interplay between manifold-learning techniques, which can extract intrinsic
order from observations of complex dynamics, and systems modeling considerations …