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1Transformers for modeling physical systems
N. Geneva, and N. Zabaras. Neural Networks, (February 2022)
a year ago by @jascal_panetzky
show all tags
Physics,
modeling,
Surrogate
learning,
Koopman,
Transformers
Deep
Self-attention,
ecomodelling
Physics,modeling,Surrogatelearning,Koopman,TransformersDeepSelf-attention,ecomodelling
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2Transformer models: an introduction and catalog
X. Amatriain. (May 2023)arXiv:2302.07730 cs.
a year ago by @jascal_panetzky
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Computation
and
Computer
Language
-
Science
ecomodelling
ComputationandComputerLanguage-Scienceecomodelling
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1Towards global empirical upscaling of FLUXNET eddy covariance observations: validation of a model tree ensemble approach using a biosphere model
M. Jung, M. Reichstein, and A. Bondeau. Biogeosciences, 6 (10): 2001--2013 (October 2009)Publisher: Copernicus GmbH.
a year ago by @jascal_panetzky
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imported
ecomodelling
importedecomodelling
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1Tiny Transformers for Environmental Sound Classification at the Edge
D. Elliott, C. Otero, S. Wyatt, and E. Martino. (March 2021)arXiv:2103.12157 cs, eess.
a year ago by @jascal_panetzky
show all tags
Learning,
Sound,
Systems
-
Processing
Science
Machine
Engineering
Electrical
and
Computer
Speech
Audio
ecomodelling
Learning,Sound,Systems-ProcessingScienceMachineEngineeringElectricalandComputerSpeechAudioecomodelling
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1Three decades of multi-dimensional change in global leaf phenology
R. Buitenwerf, L. Rose, and S. Higgins. Nature Climate Change, 5 (4): 364--368 (2015)
a year ago by @jascal_panetzky
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ecoapplication
ecomodelling
ecoapplicationecomodelling
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1The three major axes of terrestrial ecosystem function
M. Migliavacca, T. Musavi, M. Mahecha, J. Nelson, J. Knauer, D. Baldocchi, O. Perez-Priego, R. Christiansen, J. Peters, K. Anderson and 1 other author(s). Nature, 598 (7881): 468--472 (2021)
a year ago by @jascal_panetzky
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ecoapplication
ecomodelling
ecoapplicationecomodelling
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1The distribution of tree and grass roots in savannas in relation to soil nitrogen and water
E. February, and S. Higgins. South African Journal of Botany, 76 (3): 517--523 (2010)
a year ago by @jascal_panetzky
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ecoapplication
higgins
ecomodelling
ecoapplicationhigginsecomodelling
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1The current and future uses of machine learning in ecosystem service research
M. Scowen, I. Athanasiadis, J. Bullock, F. Eigenbrod, and S. Willcock. Science of The Total Environment, (December 2021)
a year ago by @jascal_panetzky
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Validation
ecostudy
Methodology
data
services
Ecosystem
learning
Data-driven
modelling
Big
ecomodelling
Machine
ValidationecostudyMethodologydataservicesEcosystemlearningData-drivenmodellingBigecomodellingMachine
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3Spherical Fourier Neural Operators: Learning Stable Dynamics on the Sphere
B. Bonev, T. Kurth, C. Hundt, J. Pathak, M. Baust, K. Kashinath, and A. Anandkumar. (2023)
8 months ago by @jascal_panetzky
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ecoapplication
ecomodelling
ecoapplicationecomodelling
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2Skilful precipitation nowcasting using deep generative models of radar
S. Ravuri, K. Lenc, M. Willson, D. Kangin, R. Lam, P. Mirowski, M. Fitzsimons, M. Athanassiadou, S. Kashem, S. Madge and 10 other author(s). Nature, 597 (7878): 672--677 (September 2021)Number: 7878 Publisher: Nature Publishing Group.
a year ago by @jascal_panetzky
show all tags
sciences
Environmental
science,
Computer
ecomodelling
sciencesEnvironmentalscience,Computerecomodelling
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2Skilful nowcasting of extreme precipitation with NowcastNet
Y. Zhang, M. Long, K. Chen, L. Xing, R. Jin, M. Jordan, and J. Wang. Nature, (July 2023)Publisher: Nature Publishing Group.
a year ago by @jascal_panetzky
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science
science,
Computer
Computational
Atmospheric
ecomodelling
sciencescience,ComputerComputationalAtmosphericecomodelling
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2Shifts in vegetation activity of terrestrial ecosystems attributable to climate trends
S. Higgins, T. Conradi, and E. Muhoko. Nature Geoscience, 16 (2): 147--153 (2023)
a year ago by @jascal_panetzky
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ecoapplication
ecomodelling
ecoapplicationecomodelling
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1Savanna vegetation-fire-climate relationships differ among continents
C. Lehmann, T. Anderson, M. Sankaran, S. Higgins, S. Archibald, W. Hoffmann, N. Hanan, R. Williams, R. Fensham, J. Felfili and 1 other author(s). Science, 343 (6170): 548--552 (2014)
a year ago by @jascal_panetzky
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ecoapplication
ecomodelling
ecoapplicationecomodelling
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1Revealing patterns of nocturnal migration using the European weather radar network
C. Nilsson, A. Dokter, L. Verlinden, J. Shamoun-Baranes, B. Schmid, P. Desmet, S. Bauer, J. Chapman, J. Alves, P. Stepanian and 1 other author(s). Ecography, 42 (5): 876--886 (2019)
a year ago by @jascal_panetzky
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ecoapplication
ecomodelling
ecoapplicationecomodelling
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1Recent decline in the global land evapotranspiration trend due to limited moisture supply
M. Jung, M. Reichstein, P. Ciais, S. Seneviratne, J. Sheffield, M. Goulden, G. Bonan, A. Cescatti, J. Chen, R. De Jeu and 23 other author(s). Nature, 467 (7318): 951--954 (October 2010)
a year ago by @jascal_panetzky
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imported
ecomodelling
importedecomodelling
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1Quantifying interregional flows of multiple ecosystem services--A case study for Germany
J. Kleemann, M. Schröter, K. Bagstad, C. Kuhlicke, T. Kastner, D. Fridman, C. Schulp, S. Wolff, J. Mart\'ınez-López, T. Koellner and 1 other author(s). Global Environmental Change, (2020)
a year ago by @jascal_panetzky
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ecoapplication
ecomodelling
ecoapplicationecomodelling
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1Predicting plant migration rates in a changing world: the role of long-distance dispersal
S. Higgins, and D. Richardson. The American Naturalist, 153 (5): 464--475 (1999)
a year ago by @jascal_panetzky
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ecoapplication
ecomodelling
ecoapplicationecomodelling
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3Precipitation Nowcasting with Satellite Imagery
V. Lebedev, V. Ivashkin, I. Rudenko, A. Ganshin, A. Molchanov, S. Ovcharenko, R. Grokhovetskiy, I. Bushmarinov, and D. Solomentsev. Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, page 2680--2688. Anchorage AK USA, ACM, (July 2019)
a year ago by @jascal_panetzky
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imported
ecomodelling
importedecomodelling
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1Next-generation dynamic global vegetation models: learning from community ecology
S. Scheiter, L. Langan, and S. Higgins. New Phytologist, 198 (3): 957--969 (2013)
a year ago by @jascal_panetzky
show all tags
ecoapplication
ecomodelling
ecoapplicationecomodelling
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1Multimodal Learning with Transformers: A Survey
P. Xu, X. Zhu, and D. Clifton. (May 2023)arXiv:2206.06488 cs.
a year ago by @jascal_panetzky
show all tags
Learning
and
Vision
Computer
-
Pattern
Science
ecomodelling
Machine
Recognition,
LearningandVisionComputer-PatternScienceecomodellingMachineRecognition,
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1Machine-Learned Climate Model Corrections From a Global Storm-Resolving Model: Performance Across the Annual Cycle
A. Kwa, S. Clark, B. Henn, N. Brenowitz, J. McGibbon, O. Watt-Meyer, W. Perkins, L. Harris, and C. Bretherton. Journal of Advances in Modeling Earth Systems, 15 (5): e2022MS003400 (2023)\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1029/2022MS003400.
a year ago by @jascal_panetzky
show all tags
coarse-graining,
storm-resolving
modeling,
learning,
machine
climate
global
model
ecomodelling
coarse-graining,storm-resolvingmodeling,learning,machineclimateglobalmodelecomodelling
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2Machine Learning for Precipitation Nowcasting from Radar Images
S. Agrawal, L. Barrington, C. Bromberg, J. Burge, C. Gazen, and J. Hickey. (December 2019)arXiv:1912.12132 cs, stat.
a year ago by @jascal_panetzky
show all tags
Learning,
Learning
and
Vision
Computer
-
Statistics
Pattern
Science
ecomodelling
Machine
Recognition,
Learning,LearningandVisionComputer-StatisticsPatternScienceecomodellingMachineRecognition,
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1Machine learning for ecosystem services
S. Willcock, J. Mart\'ınez-López, D. Hooftman, K. Bagstad, S. Balbi, A. Marzo, C. Prato, S. Sciandrello, G. Signorello, B. Voigt and 1 other author(s). Ecosystem services, (2018)
a year ago by @jascal_panetzky
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ecoapplication
ecomodelling
ecoapplicationecomodelling
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1Machine learning and artificial intelligence to aid climate change research and preparedness
C. Huntingford, E. Jeffers, M. Bonsall, H. Christensen, T. Lees, and H. Yang. Environmental Research Letters, 14 (12): 124007 (2019)
a year ago by @jascal_panetzky
show all tags
ecostudy
ecomodelling
ecostudyecomodelling
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2Influence of competition and rainfall manipulation on the growth responses of savanna trees and grasses
E. February, S. Higgins, W. Bond, and L. Swemmer. Ecology, 94 (5): 1155--1164 (2013)
a year ago by @jascal_panetzky
show all tags
ecoapplication
higgins
ecomodelling
ecoapplicationhigginsecomodelling
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3Impacts of climate change on the vegetation of Africa: an adaptive dynamic vegetation modelling approach
S. Scheiter, and S. Higgins. Global Change Biology, 15 (9): 2224--2246 (2009)
a year ago by @jascal_panetzky
show all tags
ecoapplication
higgins
ecomodelling
ecoapplicationhigginsecomodelling
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1Identifying Dynamic Memory Effects on Vegetation State Using Recurrent Neural Networks
B. Kraft, M. Jung, M. Körner, C. Requena Mesa, J. Cortés, and M. Reichstein. Frontiers in Big Data, (2019)
a year ago by @jascal_panetzky
show all tags
ecolstm
ecoapplication
climate
ecomodelling
ecolstmecoapplicationclimateecomodelling
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1Identifying dynamic memory effects on vegetation state using recurrent neural networks
B. Kraft, M. Jung, M. Körner, C. Requena Mesa, J. Cortés, and M. Reichstein. Frontiers in big Data, (2019)
10 months ago by @jascal_panetzky
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ecoapplication
ecomodelling
ecoapplicationecomodelling
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1Harnessing deep learning in ecology: An example predicting bark beetle outbreaks
W. Rammer, and R. Seidl. Frontiers in plant science, (2019)
a year ago by @jascal_panetzky
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ecocnn
ecoapplication
ecomodelling
ecocnnecoapplicationecomodelling
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4Global Vegetation Modeling with Pre-Trained Weather Transformers
P. Janetzky, F. Gallusser, S. Hentschel, A. Hotho, and A. Krause. (2024)
7 months ago by @jascal_panetzky
show all tags
myown
author:HOTHO
author:JANETZKY
author:GALLUSSER
ecoapplication
author:KRAUSE
ecomodelling
myownauthor:HOTHOauthor:JANETZKYauthor:GALLUSSERecoapplicationauthor:KRAUSEecomodelling
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1Global patterns of land-atmosphere fluxes of carbon dioxide, latent heat, and sensible heat derived from eddy covariance, satellite, and meteorological observations
M. Jung, M. Reichstein, H. Margolis, A. Cescatti, A. Richardson, M. Arain, A. Arneth, C. Bernhofer, D. Bonal, J. Chen and 14 other author(s). Journal of Geophysical Research: Biogeosciences, (2011)\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1029/2010JG001566.
a year ago by @jascal_panetzky
show all tags
FLUXNET,
interannnual
ensembles,
variability,
tree
model
upscaling
energy,
carbon,
ecomodelling
FLUXNET,interannnualensembles,variability,treemodelupscalingenergy,carbon,ecomodelling
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1Global ensemble projections reveal trophic amplification of ocean biomass declines with climate change
H. Lotze, D. Tittensor, A. Bryndum-Buchholz, T. Eddy, W. Cheung, E. Galbraith, M. Barange, N. Barrier, D. Bianchi, J. Blanchard and 25 other author(s). Proceedings of the National Academy of Sciences, 116 (26): 12907--12912 (June 2019)Company: National Academy of Sciences Distributor: National Academy of Sciences Institution: National Academy of Sciences Label: National Academy of Sciences Publisher: Proceedings of the National Academy of Sciences.
a year ago by @jascal_panetzky
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imported
ecomodelling
importedecomodelling
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1From hydrometeorology to river water quality: can a deep learning model predict dissolved oxygen at the continental scale?
W. Zhi, D. Feng, W. Tsai, G. Sterle, A. Harpold, C. Shen, and L. Li. Environmental science & technology, 55 (4): 2357--2368 (2021)
a year ago by @jascal_panetzky
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ecolstm
ecoapplication
econn
ecomodelling
ecolstmecoapplicationeconnecomodelling
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4FourCastNet: A Global Data-driven High-resolution Weather Model using Adaptive Fourier Neural Operators
J. Pathak, S. Subramanian, P. Harrington, S. Raja, A. Chattopadhyay, M. Mardani, T. Kurth, D. Hall, Z. Li, K. Azizzadenesheli and 3 other author(s). (February 2022)arXiv:2202.11214 physics.
a year ago by @jascal_panetzky
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ecoapplication
ecomodelling
ecoapplicationecomodelling
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1Evaluation of the convection-resolving climate modeling approach on continental scales
D. Leutwyler, D. Lüthi, N. Ban, O. Fuhrer, and C. Schär. Journal of Geophysical Research: Atmospheres, 122 (10): 5237--5258 (2017)\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1002/2016JD026013.
a year ago by @jascal_panetzky
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lightning,
cycle,
unit,
regional
resolving,
climate
graphics
convection,
convection
moist
diurnal
processing
model
ecomodelling
lightning,cycle,unit,regionalresolving,climategraphicsconvection,convectionmoistdiurnalprocessingmodelecomodelling
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1Ensembles of data-efficient vision transformers as a new paradigm for automated classification in ecology
S. Kyathanahally, T. Hardeman, M. Reyes, E. Merz, T. Bulas, P. Brun, F. Pomati, and M. Baity-Jesi. Scientific Reports, 12 (1): 18590 (2022)
a year ago by @jascal_panetzky
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ecoformer
ecoapplication
ecomodelling
ecoformerecoapplicationecomodelling
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1Effects of four decades of fire manipulation on woody vegetation structure in savanna
S. Higgins, W. Bond, E. February, A. Bronn, D. Euston-Brown, B. Enslin, N. Govender, L. Rademan, S. O'Regan, A. Potgieter and 1 other author(s). Ecology, 88 (5): 1119--1125 (2007)
a year ago by @jascal_panetzky
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ecoapplication
ecomodelling
ecoapplicationecomodelling
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1ECMWF short-term prediction accuracy improvement by deep learning
J. Frnda, M. Durica, J. Rozhon, M. Vojtekova, J. Nedoma, and R. Martinek. Scientific Reports, 12 (1): 7898 (May 2022)Number: 1 Publisher: Nature Publishing Group.
a year ago by @jascal_panetzky
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Environmental
Climate
computing
and
Mathematics
sciences,
ecomodelling
EnvironmentalClimatecomputingandMathematicssciences,ecomodelling
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4Do red edge and texture attributes from high-resolution satellite data improve wood volume estimation in a semi-arid mountainous region?
P. Schumacher, B. Mislimshoeva, A. Brenning, H. Zandler, M. Brandt, C. Samimi, and T. Koellner. Remote Sensing, 8 (7): 540 (2016)
a year ago by @jascal_panetzky
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ecoapplication
ecomodelling
ecoapplicationecomodelling
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1Determinants of woody cover in African savannas
M. Sankaran, N. Hanan, R. Scholes, J. Ratnam, D. Augustine, B. Cade, J. Gignoux, S. Higgins, X. Le Roux, F. Ludwig and 1 other author(s). Nature, 438 (7069): 846--849 (2005)
a year ago by @jascal_panetzky
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ecoapplication
ecomodelling
ecoapplicationecomodelling
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2Deep learning for twelve hour precipitation forecasts
L. Espeholt, S. Agrawal, C. Sønderby, M. Kumar, J. Heek, C. Bromberg, C. Gazen, R. Carver, M. Andrychowicz, J. Hickey and 2 other author(s). Nature Communications, 13 (1): 5145 (September 2022)Number: 1 Publisher: Nature Publishing Group.
a year ago by @jascal_panetzky
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science
system
Earth
Climate
modelling,
and
science,
Statistics,
Computational
Atmospheric
ecomodelling
sciencesystemEarthClimatemodelling,andscience,Statistics,ComputationalAtmosphericecomodelling
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3Deep Learning for Precipitation Nowcasting: A Benchmark and A New Model
X. Shi, Z. Gao, L. Lausen, H. Wang, D. Yeung, W. Wong, and W. WOO. Advances in Neural Information Processing Systems, 30, Curran Associates, Inc., (2017)
a year ago by @jascal_panetzky
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imported
ecomodelling
importedecomodelling
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4Deep learning for multi-year ENSO forecasts
Y. Ham, J. Kim, and J. Luo. Nature, 573 (7775): 568--572 (September 2019)Number: 7775 Publisher: Nature Publishing Group.
a year ago by @jascal_panetzky
show all tags
sciences
science,
dynamics,
Ocean
Computational
Atmospheric
ecomodelling
sciencesscience,dynamics,OceanComputationalAtmosphericecomodelling
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1Deep learning and process understanding for data-driven Earth system science
M. Reichstein, G. Camps-Valls, B. Stevens, M. Jung, J. Denzler, N. Carvalhais, and f. Prabhat. Nature, 566 (7743): 195--204 (2019)
a year ago by @jascal_panetzky
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ecostudy
ecomodelling
ecostudyecomodelling
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5Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting
X. SHI, Z. Chen, H. Wang, D. Yeung, W. Wong, and W. WOO. Advances in Neural Information Processing Systems, 28, Curran Associates, Inc., (2015)
a year ago by @jascal_panetzky
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imported
ecomodelling
importedecomodelling
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2Can Machines Learn to Predict Weather? Using Deep Learning to Predict Gridded 500-hPa Geopotential Height From Historical Weather Data
J. Weyn, D. Durran, and R. Caruana. Journal of Advances in Modeling Earth Systems, 11 (8): 2680--2693 (2019)\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1029/2019MS001705.
a year ago by @jascal_panetzky
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learning,
network,
deep
neural
machine
prediction
weather
ecomodelling
learning,network,deepneuralmachinepredictionweatherecomodelling
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3Can deep learning beat numerical weather prediction?
M. Schultz, C. Betancourt, B. Gong, F. Kleinert, M. Langguth, L. Leufen, A. Mozaffari, and S. Stadtler. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 379 (2194): 20200097 (February 2021)Publisher: Royal Society.
a year ago by @jascal_panetzky
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recognition,
learning,
deep
prediction,
machine
AI
pattern
spatiotemporal
numerical
weather
ecomodelling
recognition,learning,deepprediction,machineAIpatternspatiotemporalnumericalweatherecomodelling
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1BioTransformer: a comprehensive computational tool for small molecule metabolism prediction and metabolite identification
Y. Djoumbou-Feunang, J. Fiamoncini, A. Gil-de-la Fuente, R. Greiner, C. Manach, and D. Wishart. Journal of Cheminformatics, 11 (1): 2 (January 2019)
a year ago by @jascal_panetzky
show all tags
pathway,
degradation,
spectrometry,
Enzyme-substrate
Metabolic
Metabolism
system,
classification
Machine
learning,
identification,
prediction,
Knowledge-based
Mass
Microbial
Metabolite
specificity,
Structure-based
Biotransformation,
ecomodelling
pathway,degradation,spectrometry,Enzyme-substrateMetabolicMetabolismsystem,classificationMachinelearning,identification,prediction,Knowledge-basedMassMicrobialMetabolitespecificity,Structure-basedBiotransformation,ecomodelling
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1Automatic detection of migrating soaring bird flocks using weather radars by deep learning
I. Schekler, T. Nave, I. Shimshoni, and N. Sapir. Methods in Ecology and Evolution, (2023)
a year ago by @jascal_panetzky
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ecocnn
ecoapplication
ecomodelling
ecocnnecoapplicationecomodelling
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1Atmospheric CO2 forces abrupt vegetation shifts locally, but not globally
S. Higgins, and S. Scheiter. Nature, 488 (7410): 209--212 (2012)
a year ago by @jascal_panetzky
show all tags
ecoapplication
ecomodelling
ecoapplicationecomodelling
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