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pro vyhledávání: '"Shea, Daniel"'
Autor:
Yu, Menghan, Kulhare, Sourabh, Mehanian, Courosh, Delahunt, Charles B, Shea, Daniel E, Laverriere, Zohreh, Shah, Ishan, Horning, Matthew P
Acquiring large quantities of data and annotations is known to be effective for developing high-performing deep learning models, but is difficult and expensive to do in the healthcare context. Adding synthetic training data using generative models of
Externí odkaz:
http://arxiv.org/abs/2310.03608
Autor:
Shea, Daniel, Casey, Stephen
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 generated by a physical system is one that minimizes both predictive e
Externí odkaz:
http://arxiv.org/abs/2107.09511
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 states can be inferred directly without expensive simulations. The use of a
Externí odkaz:
http://arxiv.org/abs/2105.12295
Autor:
Shea, Daniel E., Giridharagopal, Rajiv, Ginger, David S., Brunton, Steven L., Kutz, J. Nathan
Publikováno v:
IEEE Access, vol. 9, pp. 83453-83466, 2021
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 transform, multi-resolution analysis, and Bayesian spectral analysis, we propo
Externí odkaz:
http://arxiv.org/abs/2104.01293
Boundary value problems (BVPs) play a central role in the mathematical analysis of constrained physical systems subjected to external forces. Consequently, BVPs frequently emerge in nearly every engineering discipline and span problem domains includi
Externí odkaz:
http://arxiv.org/abs/2101.07206