Friday, March 15, 2019
Dynamic mode decomposition has emerged as a leading technique to identify spatiotemporal coherent structures from high-dimensional data, benefiting from a strong connection to nonlinear dynamical syst
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Wednesday, March 6, 2019
In this article, we develop a new methodology, Hybrid-Sparse Identification of Nonlinear Dynamics, which identifies separate nonlinear dynamical regimes, employs information theory to manage uncertain
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Friday, November 30, 2018
Our flexible modeling framework directly informs programmatic decision-making by identifying age-parity-state subgroups with large rates of unmet need, highlights conflicting trends across survey inst
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Saturday, November 17, 2018
This work improves the convergence rate of dwSSA by using data generated during simulation
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Friday, August 10, 2018

In this article, we develop a new methodology, Hybrid-Sparse Identification of Nonlinear Dynamics (Hybrid-SINDy), which identifies separate nonlinear dynamical regimes, employs information theory t

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Tuesday, July 17, 2018

In this paper, we provide deterministic non-asymptotic error bounds for fitting a linear model to the observed time-series data, with a particular attention to the role of symmetry and eigenvalue m

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Saturday, February 24, 2018

We develop a new generalization of Koopman operator theory that incorporates the effects of inputs and control.

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Wednesday, November 29, 2017
In this work, we offer a novel perspective on the EBOV epidemic in Sierra Leone that uses individual virus genome sequences to inform population-level, spatial models.
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Saturday, October 21, 2017
Dynamic mode decomposition has emerged as a leading technique to identify spatiotemporal coherent structures from high-dimensional data, benefiting from a strong connection to nonlinear dynamical syste
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Wednesday, August 2, 2017

The main focus of this work is on the use of PSPO to maximize the pseudo-likelihood of a stochastic epidemiological model to data from a 1861 measles outbreak in Hagelloch, Germany.

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