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pro vyhledávání: '"Ott, Edward"'
This paper explores the potential of a hybrid modeling approach that combines machine learning (ML) with conventional physics-based modeling for weather prediction beyond the medium range. It extends the work of Arcomano et al. (2022), which tested t
Externí odkaz:
http://arxiv.org/abs/2405.19518
Autor:
Wikner, Alexander, Harvey, Joseph, Girvan, Michelle, Hunt, Brian R., Pomerance, Andrew, Antonsen, Thomas, Ott, Edward
Recent work has shown that machine learning (ML) models can be trained to accurately forecast the dynamics of unknown chaotic dynamical systems. Short-term predictions of the state evolution and long-term predictions of the statistical patterns of th
Externí odkaz:
http://arxiv.org/abs/2211.05262
Autor:
Patel, Dhruvit, Ott, Edward
In this paper we consider the machine learning (ML) task of predicting tipping point transitions and long-term post-tipping-point behavior associated with the time evolution of an unknown (or partially unknown), non-stationary, potentially noisy and
Externí odkaz:
http://arxiv.org/abs/2207.00521
Publikováno v:
Physical Review Research 4, no. 2 (2022): 023167
Machine learning (ML) has found widespread application over a broad range of important tasks. To enhance ML performance, researchers have investigated computational architectures whose physical implementations promise compactness, high-speed executio
Externí odkaz:
http://arxiv.org/abs/2204.07036
Autor:
Srinivasan, Keshav, Coble, Nolan, Hamlin, Joy, Antonsen, Thomas, Ott, Edward, Girvan, Michelle
Forecasting the dynamics of large complex networks from previous time-series data is important in a wide range of contexts. Here we present a machine learning scheme for this task using a parallel architecture that mimics the topology of the network
Externí odkaz:
http://arxiv.org/abs/2108.12129
Electromagnetic environments are becoming increasingly complex and congested, creating a growing challenge for systems that rely on electromagnetic waves for communication, sensing, or imaging, particularly in reverberating environments. The use of p
Externí odkaz:
http://arxiv.org/abs/2103.13500
Autor:
Wikner, Alexander, Pathak, Jaideep, Hunt, Brian R., Szunyogh, Istvan, Girvan, Michelle, Ott, Edward
We consider the problem of data-assisted forecasting of chaotic dynamical systems when the available data is in the form of noisy partial measurements of the past and present state of the dynamical system. Recently there have been several promising d
Externí odkaz:
http://arxiv.org/abs/2102.07819
Autor:
Wikner, Alexander, Harvey, Joseph, Girvan, Michelle, Hunt, Brian R., Pomerance, Andrew, Antonsen, Thomas, Ott, Edward
Publikováno v:
In Neural Networks February 2024 170:94-110
Publikováno v:
Phys. Rev. X 11, 031014 (2021)
We devise a machine learning technique to solve the general problem of inferring network links that have time-delays. The goal is to do this purely from time-series data of the network nodal states. This task has applications in fields ranging from a
Externí odkaz:
http://arxiv.org/abs/2010.15289