46 citations · 57 across the 5 of their papers we have counts for
5 papers
An Information Theory Approach to Physical Domain Discovery
Daniel Shea, Stephen Casey
The project of physics discovery is often equivalent to finding the most concise description of a physical system. The description with optimum predictive capability for a dataset…
Operator Autoencoders: Learning Physical Operations on Encoded Molecular Graphs
Willis Hoke, Daniel Shea, Stephen Casey
Molecular dynamics simulations produce data with complex nonlinear dynamics. If the timestep behavior of such a dynamic system can be represented by a linear operator, future state…
Extraction of instantaneous frequencies and amplitudes in nonstationary time-series data
Daniel E. Shea, Rajiv Giridharagopal, David S. Ginger +2
Time-series analysis is critical for a diversity of applications in science and engineering. By leveraging the strengths of modern gradient descent algorithms, the Fourier transfor…
DeepGreen: Deep Learning of Green's Functions for Nonlinear Boundary Value Problems
Craig R. Gin, Daniel E. Shea, Steven L. Brunton +1
Boundary value problems (BVPs) play a central role in the mathematical analysis of constrained physical systems subjected to external forces. Consequently, BVPs frequently emerge i…
SINDy-BVP: Sparse Identification of Nonlinear Dynamics for Boundary Value Problems
Daniel E. Shea, Steven L. Brunton, J. Nathan Kutz
We develop a data-driven model discovery and system identification technique for spatially-dependent boundary value problems (BVPs). Specifically, we leverage the sparse identifica…