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Overall, the key features of the proposed approach are two-fold: (i) we propose a novel architecture for the stochastic representation of two dimensional (2D) geophysical dynamics based on a neural networks ... From this point of view, the recent advances in machine learning and especially neural networks and deep learning can provide a new infrastructure for dynamical modeling and interpolation within a data-driven ... Conflicts of Interest: The authors declare no conflict of interest. ...doi:10.3390/rs10121864 fatcat:ytgmlxee5fejlerlhs3vb52iui
assimilation of geophysical fields from satellite-derived remote sensing data. ... The forecasting and reconstruction of ocean and atmosphere dynamics from satellite observation time series are key challenges. ... In this work, we investigate neural network representations for dynamical systems. Neural networks are currently the state-of-the-art techniques for a wide range of machine learning issues. ...arXiv:1806.00144v1 fatcat:hc4iumya5fcdvfp2fcnhsjcshe
The machine learning representation of convection, which we call the Cloud Brain (CBRAIN), can skillfully predict many of the convective heating, moistening, and radiative features of superparameterization ... in climate models that are built "top-down," that is, by learning salient features of convection from unusually explicit simulations. ... In particular, we use a version of stochastic gradient descent called Adam (Kingma & Ba, 2014) . How much to step down the gradient is determined by the learning rate. ...doi:10.1029/2018gl078202 fatcat:rvfln4xtqvfc7bloyv5bu5vkru
Zhang 965 Learning Effective RGB-D Representations for Scene Recognition ..... X. Song, S. Jiang, L. Herranz, and C. ... Kautz 723 Radar Imaging, Remote Sensing, and Geophysical Imaging Hyperspectral Imagery Classification via Stochastic HHSVMs ................................................................ ............ ...doi:10.1109/tip.2018.2878280 fatcat:cwalaxbmvfd3xmp5wdn2kwsk3e
Finally, the representation of endogenous learning is highly stylized and focuses on a subset of the relevant uncertainties. ... These studies broke important ground, but can still be considerably improved in aspects such as the representation of geophysical dynamics, diverse expert assessments, and the resulting impacts on the ...doi:10.1257/aer.p20161103 fatcat:sq2tgyu3xnhitp3og5yefcsoj4
Formal program development ........................ * 68095 Foundations of knowledge representation and reasoning skewers aude biaaiaasaeemabnenss tanewaamesadxeweose een * 68142 Foundations of software ... 83002 Static Analysis, 3rd International, WSA °93 .... * 68005 Theoretical Foundations of Knowledge Representation Oted TROMSG oo 5.ssccss vscccecseccscavecceese * 68142 ...
Such deep learning models if trained on observations can mitigate certain biases in current state-of-the-art weather models, some of which stem from inaccurate representation of subgrid-scale processes ... re-trained, using transfer learning, on a small number of noisy observations from a perfect simulation. ... Introduction A surge of interest in building deep learning-based data-driven models for complex systems such as chaotic dynamical systems [1, 2] , fully turbulent flow  , and weather and climate models ...arXiv:2205.04601v1 fatcat:6ltfrr4uovavbi3hk6iz3xxcze
This paper addresses variational data assimilation from a learning point of view. ... A key feature of the proposed end-to-end learning architecture is that we may train the NN models using both supervised and unsupervised strategies. ... Representation learning for geophysical dynamics: As stated in the introduction, the data-driven identification of representations of geophysical dynamics is very active research area. ...arXiv:2007.12941v1 fatcat:ibviym7ojzaxtgvflmzg6a3mvm
Despite remarkable new developments in stochastic hydrology and adaptations of advanced methods from operations research, stochastic control, and artificial intelligence, solutions of complex real-world ... A paradigm shift is introduced: an adaptation of new methods of intelligent control that will relax the dependency on rigid, computer-intensive, stochastic PDE, and will shift the emphasis to a goal-oriented ... Dong Zhang (U. of Oklahoma), Dr. S.P. Neumann (U. of Arozina), and Dr. Larry Lake (U. of Texas). ...doi:10.1007/s10040-004-0424-3 fatcat:h4bzzrojfjehncrpxnihapyjka
The authors also include historical notes throughout the text giving brief summaries of the history of probability theory and the main researchers in analysis, set theory, probability theory, and dynamic ... Presentation of the basic facts is complemented by practical interpretation of probability as a frequency of occurrence. ... Mathematical modelling of real-world processes leads, in general, to nonlinear deterministic and stochastic dynamic systems. ...doi:10.1007/s00024-013-0739-x fatcat:mkob5dhrsfbjlkebdjxsmgo3ny
Our results suggest that specific dynamic structures of auroras are highly correlated with GNSS phase scintillations. ... In this paper we use a multi-scale residual autoencoder (Res-AE) to show the correlation between specific dynamic structures of the aurora and the magnitude of the GNSS phase scintillations (σ_ϕ). ... Aurorae are highly dynamic phenomena that have long been thought to correlate with amplitude and phase scintillations due to similar geophysical drivers causing these phenomena. ...arXiv:1910.03085v1 fatcat:rujp7ulrbrfblns42hvoxpufy4
Machine Learning and Data Mining Approaches to Climate Science
Combining machine learning and stochastic filtering methods Data assimilation for dynamical systems is generally stated according to the following state space model (see e.g. ... From a methodological point of view, analog techniques provide nonparametric representations. ...doi:10.1007/978-3-319-17220-0_1 fatcat:7dza7ahj25d2dexxdovo7fb2ri
In earth science, the reconstruction of the dynamics of a given state or process from a sequence of partial and noisy observations is referred to as a data assimilation issue. ... By contrast, statistical data assimilation schemes generally relies on iterative formulations of stochastic filtering techniques such as Kalman and particle filters. ... Representation Learning for Data Assimilation As stated in the introduction, the data-driven identification of representations of geophysical dynamics is a very active research area. ...doi:10.1029/2021ms002572 fatcat:kt3jrxqugnhvfioqurngf7vvly
Modeling geophysical processes as low-dimensional dynamical systems and regressing their vector field from data is a promising approach for learning emulators of such systems. ... efficient geophysical forecasting for a large diversity of processes. ... Department of Energy ...arXiv:2103.10935v2 fatcat:qyyzgudvvfdzhb6ki2w7zhplde
NY-X) *Topics in geophysical fluid dynamics: atmospheric dynamics, dynamo theory, and climate dynamics. ... ISBN 0-387-96475-4 This book treats four separate but related topics in geophysical fluid dynamics, although the last topic (climate dynamics) is not really fundamentally fluid dynamical in nature. ...
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