Results 61 to 70 of about 55,940 (168)
Physics‐Informed Neural Networks for Battery Degradation Prediction Under Random Walk Operations
Quality and Reliability Engineering International, EarlyView.ABSTRACT This study addresses the challenge of predicting the state of health (SoH) and capacity degradation in Battery Energy Storage Systems (BESS) under highly variable conditions induced by frequent control adjustments. In environments where random walk behavior prevails due to stochastic control commands, conventional estimation methods often ...
Alaa Selim +3 more
wiley +1 more source
International Journal of Robust and Nonlinear Control, EarlyView.ABSTRACT Data‐based learning of system dynamics allows model‐based control approaches to be applied to systems with partially unknown dynamics. Gaussian process regression is a preferred approach that outputs not only the learned system model but also the variance of the model, which can be seen as a measure of uncertainty.
Daniel Landgraf +2 more
wiley +1 more source
International Journal of Robust and Nonlinear Control, EarlyView.ABSTRACT In this paper, we consider the optimal control problem for an unknown continuous‐time nonlinear system, and present a framework that integrates model‐based and model‐free methods to solve it. Each approach offers distinct advantages: model‐based techniques provide offline synthesis and data efficiency, while model‐free procedures excel at ...
Surabhi Athalye +2 more
wiley +1 more source
Isoperimetric inequalities in Euclidean convex bodies [PDF]
, 2013 In this paper we consider the problem of minimizing the relative perimeter under a volume constraint in the interior of a convex body, i.e., a compact convex set in Euclidean space with interior points.
Ritoré, Manuel, Vernadakis, Efstratios
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CAAI Transactions on Intelligence Technology, EarlyView.ABSTRACT This paper presents HealthNet, a novel framework for the dynamic optimisation of healthcare transportation networks using multi‐agent reinforcement learning. HealthNet leverages a spatiotemporal dependency module to capture complex spatiotemporal relationships in healthcare demand and resource allocation patterns, combined with centralised ...
Jianhui Lv +3 more
wiley +1 more source
SDFs from Unoriented Point Clouds using Neural Variational Heat Distances
Computer Graphics Forum, EarlyView.We propose a novel variational approach for computing neural Signed Distance Fields (SDF) from unoriented point clouds. We first compute a small time step of heat flow (middle) and then use its gradient directions to solve for a neural SDF (right). Abstract We propose a novel variational approach for computing neural Signed Distance Fields (SDF) from ...
Samuel Weidemaier +5 more
wiley +1 more source
On Metric Choice in Dimension Reduction for Fréchet Regression
International Statistical Review, EarlyView.Summary Fréchet regression is becoming a mainstay in modern data analysis for analysing non‐traditional data types belonging to general metric spaces. This novel regression method is especially useful in the analysis of complex health data such as continuous monitoring and imaging data.
Abdul‐Nasah Soale +3 more
wiley +1 more source
A Comparative Review of Specification Tests for Diffusion Models
International Statistical Review, EarlyView.Summary Diffusion models play an essential role in modelling continuous‐time stochastic processes in the financial field. Therefore, several proposals have been developed in the last decades to test the specification of stochastic differential equations.
A. López‐Pérez +3 more
wiley +1 more source
Characterization of metric spaces whose free space is isometric to $\ell_1$
, 2015 We characterize metric spaces whose Lipschitz free space is isometric to $\ell_1$. In particular, the Lipschitz free space over an ultrametric space is not isometric to $\ell_1(\Gamma)$ for any set $\Gamma$.
Dalet, Aude +2 more
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Some Remarks on Schauder Bases in Lipschitz Free Spaces [PDF]
Bulletin of the Belgian Mathematical Society - Simon Stevin, 2020 We show that the basis constant of every retractional Schauder basis on the Free space of a graph circle increases with the radius. As a consequence, there exists a uniformly discrete subset $M\subset\mathbb{R}^2$ such that $\mathcal F(M)$ does not have a retractional Schauder basis. Furthermore, we show that for any net $ N\subseteq\mathbb{R}^n$ there
openaire +3 more sources