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This paper introduces a framework for combining scientific knowledge of physics-based models with neural networks to advance scientific discovery.
Temperature as an ecological resource
John J Magnuson, Larry B Crowder, and Patricia A Medvick · 1979
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Special paper: a global biome model based on plant physiology and dominance, soil properties and climate
I Colin Prentice, Wolfgang Cramer, Sandy P Harrison, Rik Leemans, Robert A Monserud, and Allen M Solomon · 1992
Earlier work this paper cites.
Modeling chemical processes using prior knowledge and neural networks
Michael L Thompson and Mark A Kramer · 1994
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Combining semi-physical and neural network modeling: An example ofits usefulness
Urban Forssell and Peter Lindskog · 1997
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Hydrodynamics and transport for water quality modeling
James L Martin and Steven C McCutcheon · 1998
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Big data: science in the petabyte era
D Graham-Rowe, D Goldston, C Doctorow, M Waldrop, C Lynch, F Frankel, R Reid, S Nelson, D Howe, SY Rhee, et al · 2008
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Assessing the effects of climate change on aquatic invasive species
Frank J Rahel and Julian D Olden · 2008
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Blooms like it hot
Hans W Paerl and Jef Huisman · 2008
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Special issue: dealing with data
TO Jonathan, AM Gerald, et al · 2011
Cited alongside, same era.
Adadelta: an adaptive learning rate method
Matthew D Zeiler · 2012
Cited alongside, same era.
Simulations of water quality and oxythermal cisco habitat in minnesota lakes under past and future climate scenarios
Xing Fang, Shoeb R Alam, Heinz G Stefan, Liping Jiang, Peter C Jacobson, and Donald L Pereira · 2012
Cited alongside, same era.
Fragmentation and thermal risks from climate change interact to affect persistence of native trout in the colorado river basin
James J Roberts, Kurt D Fausch, Douglas P Peterson, and Mevin B Hooten · 2013
Cited alongside, same era.
Putting big data to good use in neuroscience
Terrence J Sejnowski, Patricia S Churchland, and J Anthony Movshon · 2014
Cited alongside, same era.
Debates—the future of hydrological sciences: A (common) path forward? a call to action aimed at understanding velocities, celerities and residence time distributions of the headwater hydrograph
Jeffrey J McDonnell and Keith Beven · 2014
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Glm—general lake model: Model overview and user information
MR Hipsey, LC Bruce, and DP Hamilton · 2014
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The scientists’ apprentice
Tim Appenzeller · 2017
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Nonnative trout invasions combined with climate change threaten persistence of isolated cutthroat trout populations in the southern rocky mountains
James J Roberts, Kurt D Fausch, Mevin B Hooten, and Douglas P Peterson · 2017
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Predicting cyanobacterial abundance, microcystin, and geosmin in a eutrophic drinking-water reservoir using a 14-year dataset
Ted D Harris and Jennifer L Graham · 2017
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Debates—the future of hydrological sciences: A (common) path forward? using models and data to learn: A systems theoretic perspective on the future of hydrological science
Hoshin V Gupta and Grey S Nearing · 2014
Cited alongside, same era.
Debates—the future of hydrological sciences: A (common) path forward? one water. one world. many climes. many souls
Upmanu Lall · 2014
Cited alongside, same era.
Machine learning closures for model order reduction of thermal fluids
Omer San and Romit Maulik
Cited in the paper.
Neural network closures for nonlinear model order reduction
Omer San and Romit Maulik
Cited in the paper.
Water quality data for national-scale aquatic research: The water quality portal
Emily K Read, Lindsay Carr, Laura De Cicco, Hilary A Dugan, Paul C Hanson, Julia A Hart, James Kreft, Jordan S Read, and Luke A Winslow · 2017
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Data-assisted reduced-order modeling of extreme events in complex dynamical systems
Zhong Yi Wan, Pantelis Vlachas, Petros Koumoutsakos, and Themistoklis Sapsis · 2018
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