Results 81 to 90 of about 5,322 (250)
Forecast‐Error Diagnostics in Neural Weather Models
Deep learning weather prediction models enable efficient forecast‐error diagnostics through auto‐differentiation and low computational cost. We apply grid‐point relaxation and gradient‐based error sensitivity to identify key forecast‐error sources. Results show that medium‐range forecasts in the midlatitudes benefit most from relaxing the stratosphere ...
Uroš Perkan +2 more
wiley +1 more source
The Weak‐Constraint Four‐Dimensional Variational (WC‐4DVAR) system markedly improved the analysis and forecasting of the 2024 sudden stratospheric warming (SSW) event. It reduced biases and enhanced 7–9 day predictions of temperature across the SSW life cycle, with the most pronounced gains for winds also during the critical onset period.
Shaoying Li +5 more
wiley +1 more source
Basal sliding and other processes affecting ice flow are challenging to constrain due to limited direct observations. Inversion methods, which typically fit an ice-flow model to observed surface velocities, enable the reconstruction of basal properties ...
Ivan Utkin +3 more
doaj +1 more source
Coupled observation‐operator approximations in outer‐loop coupling data assimilation
We present a methodology for coupled variational data assimilation that allows the effect of coupled observation operators whilst maintaining separate minimisations across model components. This allows us to constrain the atmosphere, ocean, and sea‐ice components of our coupled model directly from satellite radiances. We illustrate the system in action
P. A. Browne +5 more
wiley +1 more source
Topology optimization of a heat-assisted magnetic recording write head to reduce transition curvature using a binary optimization algorithm utilizing the adjoint method. [PDF]
Wautischer G +4 more
europepmc +1 more source
On the utilization of the adjoint method in microwave tomography
AbstractIn microwave imaging, the adjoint method is widely used for the efficient calculation of the update direction, which is then used to update the unknown model parameter. However, the utilization and the formulation of the adjoint method differ significantly depending on the imaging scenario and the applied optimization algorithm.
Damla Alptekin Soydan +2 more
openaire +2 more sources
Hierarchical Differentiable Fluid Simulation
We introduce a two‐step algorithm that significantly reduces memory usage for solving control problems using differentiable fluid simulation techniques: our method first optimizes for bulk forces at reduced resolution, then refines local details over sub‐domains while maintaining differentiability. In trading runtime for memory, it enables optimization
Xiangyu Kong +4 more
wiley +1 more source
Expansion of the Nodal-Adjoint Method for Simple and Efficient Computation of the 2D Tomographic Imaging Jacobian Matrix. [PDF]
Hosseinzadegan S +4 more
europepmc +1 more source
Survey on differential estimators for 3d point clouds
Abstract Recent advancements in 3D scanning technologies, including LiDAR and photogrammetry, have enabled the precise digital replication of real‐world objects. These methods are widely used in fields such as GIS, robotics, and cultural heritage. However, the point clouds generated by such scans are often noisy and unstructured, posing challenges for ...
Léo Arnal–Anger +4 more
wiley +1 more source
Non‐Rigid 3D Shape Correspondences: From Foundations to Open Challenges and Opportunities
Abstract Estimating correspondences between deformed shape instances is a long‐standing problem in computer graphics; numerous applications, from texture transfer to statistical modelling, rely on recovering an accurate correspondence map. Many methods have thus been proposed to tackle this challenging problem from varying perspectives, depending on ...
A. Zhuravlev +14 more
wiley +1 more source

