Results 91 to 100 of about 125,502 (226)
FURTHER RESULTS ON JENSEN-TYPE INEQUALITIES
Проблемы анализа, 2019 In this paper, we establish some Jensen-type inequalities for continuous functions of self-adjoint operators on complex Hilbert spaces. Furthermore, using the Cartesian decomposition of an operator, we improve the known result due to Mond and Peˇcari´c.
B. Moosavi +2 more
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Quarterly Journal of the Royal Meteorological Society, EarlyView.Daily time series of zonal‐mean zonal wind (m·s−1) at 10 hPa and 60° N from 1950 to 2021 from the ERA5 reanalysis. This shows huge variability in some seasons and very little in others. We provide evidence that high‐level observations, radiosonde and satellite, are more important during the extended winter season with its very large variability ...
Bruce Ingleby, Inna Polichtchouk
wiley +1 more source
TRANSITION FROM 2D CONTINUOUS ADJOINT LEVEL SET TOPOLOGY TO SHAPE OPTIMIZATION
Proceedings of the VII European Congress on Computational Methods in Applied Sciences and Engineering (ECCOMAS Congress 2016), 2016 The processes of topology and shape optimization are well known methods in the field of fluid mechanics. Although successful in their own rights, it is conceivable that the two methods will find choicest solutions in tandem: i.e. if shape optimization were able to improve atopologicalsolution.
Koch, J.R.L., +2 more
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Extension of Jensen's Inequality for Operators without Operator Convexity
Abstract and Applied Analysis, 2011 We give an extension of Jensen's inequality for 𝑛-tuples of self-adjoint operators, unital 𝑛-tuples of positive linear mappings, and real-valued continuous convex functions with conditions on the operators' bounds.
Jadranka Mićić +2 more
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Hierarchical Differentiable Fluid Simulation
Computer Graphics Forum, EarlyView.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
A Continuous Adjoint Framework For Vehicle Aerocoustic Optimization
, 2018 This paper presents a novel adjoint-based methodology for vehicle aeroacoustic optimization. The adjoint sensitivity map on the side mirror is computed, which indicates how its geometry should change in order to reduce the wind noise transmission into the vehicle interior.
Kapellos, C.,, Hartmann, M.,
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Discretely exact derivatives for hyperbolic PDE-constrained optimization problems discretized by the discontinuous Galerkin method [PDF]
, 2013 This paper discusses the computation of derivatives for optimization problems governed by linear hyperbolic systems of partial differential equations (PDEs) that are discretized by the discontinuous Galerkin (dG) method.
Bui-Thanh, Tan +3 more
core +1 more source
Survey on differential estimators for 3d point clouds
Computer Graphics Forum, EarlyView.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
Computer Graphics Forum, EarlyView.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
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Digital EngineeringAdvances in deep learning have impacted all areas of business, government and academia, and deep learning is expanding into domains that are beyond the scope of the standard Computer Science curriculum.
Joseph M. Worsham, Jugal K. Kalita
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