Complex systems involve monitoring, assessing, and predicting the health of various
systems within an integrated vehicle health management (IVHM) system or a larger system …

In the present work, we introduce a novel approach to enhance the precision of reduced
order models by exploiting a multi-fidelity perspective and DeepONets. Reduced models …

Recent work has demonstrated the use of sparse sensors in combination with the proper
orthogonal decomposition (POD) to produce data-driven reconstructions of the full velocity …

We propose a novel nonlinear manifold learning from snapshot data and demonstrate its
superiority over proper orthogonal decomposition (POD) for shedding-dominated shear …

The discrete empirical interpolation method (DEIM) estimates a function from its incomplete
pointwise measurements. Unfortunately, DEIM suffers large interpolation errors when few …

We propose a data-driven sensor-selection algorithm for accurate estimation of the target
variables from the selected measurements. The target variables are assumed to be …

Large-dimensional empirical data in science and engineering frequently has low-rank
structure and can be represented as a combination of just a few eigenmodes. Because of …

Many scientific problems can be formulated as sparse regression, ie, regression onto a set
of parameters when there is a desire or expectation that some of the parameters are exactly …

We propose a data-driven methodology to learn a low-dimensional actuation manifold of
controlled flows. The starting point is resolving snapshot flow data for a representative …

Accurate and efficient models of physical processes like fluid flows are crucial for
applications ranging from forecasting the weather to controlling autonomous aircraft and …