Results 81 to 90 of about 691 (194)

Short‐Term Estuarine Phytoplankton Dynamics in Response to Hurricanes Along the Gulf Coast of America: A Variational Autoencoder (VAE) Approach With Satellite and Bio‐Optical Observations

Journal of Geophysical Research: Oceans, Volume 131, Issue 3, March 2026.
Abstract Hurricanes drive diverse estuarine phytoplankton responses and can trigger cascading ecological and physicochemical impacts. Capturing these short‐term dynamics requires high spatiotemporal resolution. Here, we applied a globally‐applicable coastal ocean color algorithm, Variational Autoencoder (VAE), to Sentinel‐2 MSI imagery for chlorophyll ...
J. Li, B. Liu, J. Lou, X. Yuan, E. J. D’Sa, M. M. Baustian, M. La Peyre, A. Freeman, V. S. Martins, E. Habib   +9 more
wiley   +1 more source

Convergence of adaptive stochastic collocation with finite elements

, 2020
We consider an elliptic partial differential equation with a random diffusion parameter discretized by a stochastic collocation method in the parameter domain and a finite element method in the spatial domain. We prove for the first time convergence of a stochastic collocation algorithm which adaptively enriches the parameter space as well as refines ...
Feischl, Michael, Scaglioni, Andrea
openaire   +2 more sources

Medium‐Range Predictability of the Wintertime Bering Sea Ice Edge Using Linear Inverse Modeling

Journal of Geophysical Research: Oceans, Volume 131, Issue 3, March 2026.
Abstract Beginning in autumn, sea ice expands into the southern Bering Sea, where it remains until spring. In winter, some commercial stocks, particularly crabs, thrive in ice‐infested areas, necessitating short‐lead forecasts of the ice edge for fishers.
Christopher J. Cox, Cécile Penland
wiley   +1 more source

Modeling Transient Groundwater Flow in Unconfined Aquifers Under Dynamic Conditions Using Physics‐Informed Neural Networks

Water Resources Research, Volume 62, Issue 3, March 2026.
Abstract Deep learning neural networks (DLNNs) hold great potential for modeling groundwater flow, but their performance depends on data availability. Physics‐informed neural networks (PINNs) help to reduce the reliance of DLNNs on data by integrating physical laws into the training process. This approach is increasingly used in applications related to
Adhish Virupaksha, François Lehmann, Hussein Hoteit, Anis Younes, Marwan Fahs, Thomas Nagel   +5 more
wiley   +1 more source

Sensemaking of Process Data from Evaluation Studies of Educational Games: An Application of Cross‐Classified Item Response Theory Modeling

Journal of Educational Measurement, Volume 63, Issue 1, Spring 2026.
Abstract Process information collected from educational games can illuminate how students approach interactive tasks, complementing assessment outcomes routinely examined in evaluation studies. However, the two sources of information are historically analyzed and interpreted separately, and diagnostic process information is often underused.
Tianying Feng, Li Cai
wiley   +1 more source

Walsh function-based numerical approach for nonlinear stochastic integral equations: Application to stochastic logistic models

Boundary Value Problems
The current research study proposes an efficient numerical method for obtaining an approximate solution to nonlinear stochastic integral equations implementing the collocation method and the Walsh operational matrices.
Prit Pritam Paikaray, Sanghamitra Beuria, Nigam Chandra Parida, Mohammad Izadi   +3 more
doaj   +1 more source

STOCHASTIC FINITE ELEMENT MODEL UPDATING BASED ON POLYNOMIAL CHAOTIC EXPANSION AND KL DIVERGENCE

Jixie qiangdu, 2021
Considering the influence of structural parameter uncertainty on response and the problem of large calculation of stochastic model updating, a stochastic finite element model updating method based on polynomial chaotic expansion and KL divergence is ...
XU ZeWei, PENG ZhenRui, ZHANG YaFeng, BAI Yu   +3 more
doaj  

STOCHASTIC COLLOCATION FOR CORRELATED INPUTS [PDF]

Proceedings of the 1st International Conference on Uncertainty Quantification in Computational Sciences and Engineering (UNCECOMP 2015), 2015
Maria Navarro, Jeroen Witteveen, Joke Blom   +2 more
openaire   +1 more source

Tropical TCV as a Process Diagnostic: Connecting Probability to Processes in km–Scale Models Via Moisture Budget Statistics

Geophysical Research Letters, Volume 53, Issue 4, 28 February 2026.
Abstract Observations show a bimodal frequency distribution in total column vapor (TCV) over tropical oceans, with convective rainfall predominantly produced on the moist side of the frequency minimum between two modal peaks. Here we show a km–scale model of the tropics with explicit convection produces a bimodal TCV distribution, whereas the same ...
James Bassford, John Marsham, Ben Maybee, Douglas J. Parker   +3 more
wiley   +1 more source

A Simple Collocation-Type Approach to Numerical Stochastic Homogenization

Multiscale Modeling & Simulation
Accepted for publication in Multiscale Modeling & ...
Hauck, Moritz, Mohr, Hannah, Peterseim, Daniel   +2 more
openaire   +2 more sources