Zobrazeno 1 - 10
of 59
pro vyhledávání: '"Dunbar, Oliver"'
Autor:
Guan, Yifei, Hassanzadeh, Pedram, Schneider, Tapio, Dunbar, Oliver, Huang, Daniel Zhengyu, Wu, Jinlong, Lopez-Gomez, Ignacio
Different approaches to using data-driven methods for subgrid-scale closure modeling have emerged recently. Most of these approaches are data-hungry, and lack interpretability and out-of-distribution generalizability. Here, we use {online} learning t
Externí odkaz:
http://arxiv.org/abs/2409.04985
Randomized algorithms exploit stochasticity to reduce computational complexity. One important example is random feature regression (RFR) that accelerates Gaussian process regression (GPR). RFR approximates an unknown function with a random neural net
Externí odkaz:
http://arxiv.org/abs/2407.00584
Machine learning is playing an increasing role in hydrology, supplementing or replacing physics-based models. One notable example is the use of recurrent neural networks (RNNs) for forecasting streamflow given observed precipitation and geographic ch
Externí odkaz:
http://arxiv.org/abs/2404.14212
We propose and unify classes of different models for information propagation over graphs. In a first class, propagation is modelled as a wave which emanates from a set of known nodes at an initial time, to all other unknown nodes at later times with
Externí odkaz:
http://arxiv.org/abs/2201.07577
Data required to calibrate uncertain GCM parameterizations are often only available in limited regions or time periods, for example, observational data from field campaigns, or data generated in local high-resolution simulations. This raises the ques
Externí odkaz:
http://arxiv.org/abs/2201.06998
Autor:
Schneider, Tapio, Dunbar, Oliver R. A., Wu, Jinlong, Böttcher, Lucas, Burov, Dmitry, Garbuno-Iñigo, Alfredo, Wagner, Gregory L., Pei, Sen, Daraio, Chiara, Ferrari, Raffaele, Shaman, Jeffrey
Publikováno v:
PLoS Comput Biol 18(6): e1010171. (2022)
Testing, contact tracing, and isolation (TTI) is an epidemic management and control approach that is difficult to implement at scale because it relies on manual tracing of contacts. Exposure notification apps have been developed to digitally scale up
Externí odkaz:
http://arxiv.org/abs/2109.10970
Climate models are generally calibrated manually by comparing selected climate statistics, such as the global top-of-atmosphere energy balance, to observations. The manual tuning only targets a limited subset of observational data and parameters. Bay
Externí odkaz:
http://arxiv.org/abs/2108.00827
The increasing availability of data presents an opportunity to calibrate unknown parameters which appear in complex models of phenomena in the biomedical, physical and social sciences. However, model complexity often leads to parameter-to-data maps w
Externí odkaz:
http://arxiv.org/abs/2104.03384
Parameters in climate models are usually calibrated manually, exploiting only small subsets of the available data. This precludes both optimal calibration and quantification of uncertainties. Traditional Bayesian calibration methods that allow uncert
Externí odkaz:
http://arxiv.org/abs/2012.13262
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