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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Thursday, July 6, 2017
​Explores relations between​ mean field models for non-Markovian epidemics in networks.​
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Tuesday, May 30, 2017

​We present a universal, data-driven decomposition of chaos as an intermittently forced linear system. ​​

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Wednesday, April 26, 2017
​​​​Epidemics on modern human mobility networks are complex, but we demonstrate that population-level dynamics can be summarized with a parsimonious model based on space-fractional diffusion.​
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Wednesday, April 26, 2017
The method provides a promising new technique for discovering governing equations and physical laws in parameterized spatiotemporal systems. ​
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Tuesday, April 25, 2017

Containing the recent West African outbreak of Ebola virus (EBOV) required the deployment of substantial global resources.

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Tuesday, April 4, 2017
​​Utilizing the concept of observability, in conjunction with tools from graph theory and optimization, this paper develops an algorithm for network synthesis with privacy guarantees.​
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Thursday, January 19, 2017

​We propose an alternative data-driven method to infer networked nonlinear dynamical systems by using sparsity-promoting optimization to select a subset of nonlinear interactions representing dynam

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Tuesday, January 17, 2017
The integration of nonlinear dynamics and machine learning opens the door for principled versus heuristic methods for model construction, nonlinear control strategies, and sensor placement techniques.
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