Results 51 to 60 of about 12,007 (165)
Data‐Driven Dynamic Modal Bias Analysis and Correction for Earth System Models
Journal of Advances in Modeling Earth Systems, Volume 17, Issue 5, May 2025.Abstract Predicting Earth systems weeks or months into the future is an important yet challenging problem due to the high dimensionality, chaotic behavior, and coupled dynamics of the ocean, atmosphere, and other subsystems of the Earth. Numerical models invariably contain model error due to incomplete domain knowledge, limited capabilities of ...
S. P. McGowan +3 more
wiley +1 more source
Water Resources Research, Volume 61, Issue 5, May 2025.Abstract Understanding subsurface heterogeneity is crucial to predicting groundwater flow pathways, mixing, and other processes in aquifers and other fluid reservoirs. Despite significant effort developing geophysical tools to understand this heterogeneity, geophysical mapping of aquifer flow parameters—transmissivity (T) $(T)$, and storativity (S) $(S)
Jeremy R. Patterson, Michael Cardiff
wiley +1 more source
Orthopaedic Surgery, Volume 17, Issue 5, Page 1287-1297, May 2025.Although endoscopic technologies have been increasingly applied in lumbar fusion surgery in recent years, the advantages and disadvantages of endoscopic posterolateral fusion compared with lateral fusion remain unclear.In this study, minimally invasive lumbar fusion popular in recent years is divided into 6 types of surgery:extreme/direct lateral ...
Xijian Hu +8 more
wiley +1 more source
Deep Material Networks for Fiber Suspensions With Infinite Material Contrast
International Journal for Numerical Methods in Engineering, Volume 126, Issue 7, 15 April 2025.ABSTRACT We extend the laminate based framework of direct deep material networks (DMNs) to treat suspensions of rigid fibers in a non‐Newtonian solvent. To do so, we derive two‐phase homogenization blocks that are capable of treating incompressible fluid phases and infinite material contrast.
Benedikt Sterr +4 more
wiley +1 more source
Prediction bounds for higher order total variation regularized least squares
, 2020 We establish adaptive results for trend filtering: least squares estimation with a penalty on the total variation of $(k-1)^{\rm th}$ order differences.
Ortelli, Francesco, van de Geer, Sara
core +1 more source
Characterizations of the Sobolev space H1 on the boundary of a strongly Lipschitz domain in 3‐D
Mathematische Nachrichten, Volume 298, Issue 4, Page 1342-1355, April 2025.Abstract In this work, we investigate the Sobolev space H1(∂Ω)$\mathrm{H}^{1}(\partial \Omega)$ on a strongly Lipschitz boundary ∂Ω$\partial \Omega$, that is, Ω$\Omega$ is a strongly Lipschitz domain (not necessarily bounded). In most of the literature, this space is defined via charts and Sobolev spaces on flat domains.
Nathanael Skrepek
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Recovering Latent Signals from a Mixture of Measurements using a Gaussian Process Prior
, 2017 In sensing applications, sensors cannot always measure the latent quantity of interest at the required resolution, sometimes they can only acquire a blurred version of it due the sensor's transfer function. To recover latent signals when only noisy mixed
Guerrero, Pablo +3 more
core +1 more source
Complementary Filter‐Based Incremental Nonlinear Model Following Control Design for a Tilt‐Wing UAV
International Journal of Robust and Nonlinear Control, Volume 35, Issue 4, Page 1596-1615, 10 March 2025.ABSTRACT This article presents an incremental nonlinear model following control (INMFC) strategy for a tilt‐wing vertical take‐off and landing (VTOL) unmanned aerial vehicle (UAV). To ensure a good and robust regulation performance, a two‐loop feedback controller, based on the incremental backstepping (IBS) methodology, is used to handle uncertainties ...
Johannes Autenrieb, Hyo‐Sang Shin
wiley +1 more source
The Canadian Journal of Chemical Engineering, Volume 103, Issue 3, Page 1099-1111, March 2025.In this work we develop a data‐driven modelling approach which integrates an autoencoder‐like neural network and dynamic mode decomposition (DMD) methods, to result in a nonlinear modelling technique. In addition, we develop a quadratic programming based model predictive controller (MPC) for the proposed model and implement an observer using ...
Xiaonian Wang +3 more
wiley +1 more source
Angles between subspaces and their tangents
, 2013 Principal angles between subspaces (PABS) (also called canonical angles) serve as a classical tool in mathematics, statistics, and applications, e.g., data mining. Traditionally, PABS are introduced via their cosines.
Andrew V. Knyazev +2 more
core +2 more sources