Results 11 to 20 of about 497,884 (333)
A Parameterization of Local and Remote Tidal Mixing
Journal of Advances in Modeling Earth Systems, 2020 Vertical mixing is often regarded as the Achilles' heel of ocean models. In particular, few models include a comprehensive and energy‐constrained parameterization of mixing by internal ocean tides.
C. deLavergne +9 more
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On the Parameterization and Initialization of Diagonal State Space Models [PDF]
Neural Information Processing Systems, 2022 State space models (SSM) have recently been shown to be very effective as a deep learning layer as a promising alternative to sequence models such as RNNs, CNNs, or Transformers.
Albert Gu +3 more
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Computing Parameterized Invariants of Parameterized Petri Nets [PDF]
Fundamenta Informaticae, 2021 A fundamental advantage of Petri net models is the possibility to automatically compute useful system invariants from the syntax of the net. Classical techniques used for this are place invariants, P-components, siphons or traps. Recently, Bozga et al.
Esparza, Javier +2 more
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Dataset Condensation via Efficient Synthetic-Data Parameterization [PDF]
International Conference on Machine Learning, 2022 The great success of machine learning with massive amounts of data comes at a price of huge computation costs and storage for training and tuning.
Jang-Hyun Kim +7 more
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Advances in Applied Clifford Algebras, 2018 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Lars Tingelstad, Olav Egeland
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Parameterized Proof Complexity [PDF]
computational complexity, 2007 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Dantchev, S., Martin, B., Szeider, S.
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Whitecap Fraction Parameterization and Understanding with Deep Neural Network
Remote Sensing, 2022 Accurate calculation of the whitecap fraction is of great importance for the estimation of air-sea momentum flux, heat flux and sea-salt aerosol flux in Earth system models.
Shuyi Zhou, Fanghua Xu, Ruizi Shi
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Use of Neural Networks for Stable, Accurate and Physically Consistent Parameterization of Subgrid Atmospheric Processes With Good Performance at Reduced Precision [PDF]
Geophysical Research Letters, 2020 A promising approach to improve climate‐model simulations is to replace traditional subgrid parameterizations based on simplified physical models by machine learning algorithms that are data‐driven.
J. Yuval, P. O’Gorman, C. Hill
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Discrete and Continuous Models and Applied Computational Science, 2021 Quantum systems with a finite number of states at all times have been a primary element of many physical models in nuclear and elementary particle physics, as well as in condensed matter physics. Today, however, due to a practical demand in the area of developing quantum technologies, a whole set of novel tasks for improving our understanding of the ...
Arsen Khvedelidze +2 more
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Gaoyuan qixiang, 2023 In order to explore the relationship between CO2 flux and photosynthetically active radiation （PAR） in evergreen broad-leaved forest in the Phoenix Mountain， Zhuhai， and improve the ability of simulating CO2 flux by using PAR and meteorological factors ...
Shitong GUO, Zhigang WEI, Huan WANG
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