Results 231 to 240 of about 9,274 (303)

Research on Navigation Method for Subsea Drilling Robot Based on Inertial Navigation and Odometry. [PDF]

Sensors (Basel)
Liu Y, Zhou P, Xiao F, Li C, Li J, Chen J, Ren Z.   +6 more
europepmc   +1 more source

A Deep Learning‐Enhanced Ensemble Smoother for Non‐Gaussian Hydrological Data Assimilation Without Relying on Innovation Vectors

Journal of Geophysical Research: Machine Learning and Computation, Volume 3, Issue 3, June 2026.
Abstract Data assimilation (DA) plays a critical role in reducing simulation uncertainty in hydrological systems by leveraging available observations to estimate model states and/or parameters. Among DA methods, the ensemble smoother (ES) has emerged as an attractive option for parameter estimation due to its computational efficiency and ...
Jiangjiang Zhang, Junliang Jin, Lei Yao, Kun Zhou, Jianyun Zhang   +4 more
wiley   +1 more source

Particle Filtering-Based In-Flight Icing Detection for Unmanned Aerial Vehicles. [PDF]

Sensors (Basel)
Souanef T, Tadjine M, Horri N, Chaabeni I, Boulassel B.   +4 more
europepmc   +1 more source

Improving Global Surface Soil Moisture Prediction Through Physics‐Guided Deep Learning and Cluster‐Based Regionalization

Journal of Geophysical Research: Machine Learning and Computation, Volume 3, Issue 3, June 2026.
Abstract Surface soil moisture (SSM) is essential to the hydrological cycle and land–atmosphere interactions, and its accurate simulation is crucial for climate prediction and resource management. This study developed an innovative modeling framework for global SSM prediction by integrating physics‐guided deep learning (PGDL) and clustering‐based ...
Xuan Xi, Qianlai Zhuang
wiley   +1 more source

A selective Kalman filtering approach to online neural network updating under system drift. [PDF]

Sci Rep
Hammond JE, Soderstrom TA, Korgel BA, Baldea M.   +3 more
europepmc   +1 more source

Strongly Coupled Data Assimilation for Paleoclimate Reconstruction Using Deep Learning‐Based Models

Journal of Advances in Modeling Earth Systems, Volume 18, Issue 6, June 2026.
Abstract Coupled data assimilation (CDA) could be beneficial for paleoclimate reconstruction, but intermediate‐complexity climate models are often used. The deep learning (DL)‐based surrogate model that can realistically simulate the climate system provides alternative to CDA, so that CDA of multi‐timescale proxy data using DL‐based models is ...
Lili Lei, Haohao Sun, Zhe‐Min Tan, Liang Ning, Zhengyu Liu   +4 more
wiley   +1 more source

Search model based on Kalman Filter and Monte Carlo simulation. [PDF]

PLoS One
Liu J, Li Y, Liu X.
europepmc   +1 more source

Eruption Source Parameters in Volcanic Plume Modeling: Advances, Challenges, and Future Directions

Reviews of Geophysics, Volume 64, Issue 2, June 2026.
Abstract Accurately predicting the atmospheric dispersion of volcanic ash and gases is crucial for both scientific understanding and hazard mitigation. Estimating Eruption Source Parameters (ESP), such as mass eruption rate, plume height, duration, and particle size distribution and properties, remains challenging due to the complex nature of volcanic ...
A. Costa, A. Folch, Y. J. Suzuki, L. Mingari, G. Macedonio   +4 more
wiley   +1 more source

Integrating GAN-based machine learning with nonlinear Kalman filtering for enhanced state estimation. [PDF]

Sci Rep
Tobaly L, Yaniv E, Zalevsky Z.
europepmc   +1 more source

Identifying the State Dependence of Effective Material Properties in a Simplified Hydrologic Hillslope Model

Water Resources Research, Volume 62, Issue 6, June 2026.
Abstract Accurate representation of closure relationships, such as subsurface storage‐discharge, is essential for hydrologic modeling at the hillslope scale, but remains challenging due to the nonlinear state‐dependent nature of soil hydraulic behavior.
Marcus N. Gomes Jr., Hannes H. Bauser, Peter A. Troch   +2 more
wiley   +1 more source