1 citations · 2 across the 5 of their papers we have counts for
6 papers
Equation identification for fluid flows via physics-informed neural networks
Alexander New, Marisel Villafañe-Delgado, Charles Shugert
Scientific machine learning (SciML) methods such as physics-informed neural networks (PINNs) are used to estimate parameters of interest from governing equations and small quantiti…
Exploiting Large Neuroimaging Datasets to Create Connectome-Constrained Approaches for more Robust, Efficient, and Adaptable Artificial Intelligence
Erik C. Johnson, Brian S. Robinson, Gautam K. Vallabha +17
Despite the progress in deep learning networks, efficient learning at the edge (enabling adaptable, low-complexity machine learning solutions) remains a critical need for defense a…
Detecting Anomalous Swarming Agents with Graph Signal Processing
Kevin Schultz, Anshu Saksena, Elizabeth P. Reilly +2
Collective motion among biological organisms such as insects, fish, and birds has motivated considerable interest not only in biology but also in distributed robotic systems. In a…
Analyzing Collective Motion Using Graph Fourier Analysis
Kevin Schultz, Marisel Villafane-Delgado, Elizabeth P. Reilly +2
Collective motion in animal groups, such as swarms of insects, flocks of birds, and schools of fish, are some of the most visually striking examples of emergent behavior. Empirical…
Graph Signal Processing for Infrastructure Resilience: Suitability and Future Directions
Kevin Schultz, Marisel Villafane-Delgado, Elizabeth P. Reilly +2
Graph signal processing (GSP) is an emerging field developed for analyzing signals defined on irregular spatial structures modeled as graphs. Given the considerable literature rega…
Graph Signal Processing of Indefinite and Complex Graphs using Directed Variation
Kevin Schultz, Marisel Villafane-Delgado
In the field of graph signal processing (GSP), directed graphs present a particular challenge for the "standard approaches" of GSP to due to their asymmetric nature. The presence o…