Results 81 to 90 of about 40,316 (199)
Computer-Aided Civil and Infrastructure Engineering, EarlyView.Abstract The reliability of machine learning heavily depends on training data; however, in the field of geotechnical engineering, it is challenging to obtain diverse datasets due to economic and accessibility limitations. The aim of this study is to propose a method for generating data for use in the training phase of machine learning by combining ...
Junghee Park, Hyung‐Koo Yoon
wiley +1 more source
ON THE SET-SEMIDEFINITE REPRESENTATION OF NONCONVEX QUADRATIC PROGRAMS WITH CONE CONSTRAINTS
Croatian Operational Research Review, 2010 The well-known result stating that any non-convex quadratic problem over the non-negative orthant with some additional linear and binary constraints can be rewritten as linear problem over the cone of completely positive matrices (Burer, 2009) is ...
Gabriele Eichfelder, Janez Povh
doaj
A Physics‐Informed Learning Framework to Solve the Infinite‐Horizon Optimal Control Problem
International Journal of Robust and Nonlinear Control, Volume 35, Issue 16, Page 6932-6944, 10 November 2025.ABSTRACT We propose a physics‐informed neural networks (PINNs) framework to solve the infinite‐horizon optimal control problem of nonlinear systems. In particular, since PINNs are generally able to solve a class of partial differential equations (PDEs), they can be employed to learn the value function of the infinite‐horizon optimal control problem via
Filippos Fotiadis +1 more
wiley +1 more source
Semidefinite geometry of the numerical range
, 2010 The numerical range of a matrix is studied geometrically via the cone of positive semidefinite matrices (or semidefinite cone for short). In particular it is shown that the feasible set of a two-dimensional linear matrix inequality (LMI), an affine ...
Henrion, Didier
core +1 more source
Direct Data‐Driven State‐Feedback Control of Linear Parameter‐Varying Systems
International Journal of Robust and Nonlinear Control, Volume 35, Issue 16, Page 6955-6977, 10 November 2025.ABSTRACT The framework of linear parameter‐varying (LPV) systems has shown to be a powerful tool for the design of controllers for complex nonlinear systems using linear tools. In this work, we derive novel methods that allow us to synthesize LPV state‐feedback controllers directly from only a single sequence of data and guarantee stability and ...
Chris Verhoek +2 more
wiley +1 more source
Positive semidefiniteness of estimated covariance matrices in linear models for sample survey data
Special Matrices, 2016 Descriptive analysis of sample survey data estimates means, totals and their variances in a design framework. When analysis is extended to linear models, the standard design-based method for regression parameters includes inverse selection probabilities ...
Haslett Stephen
doaj +1 more source
Data‐Driven Inverse Design of Spinodoid Architected Materials
GAMM-Mitteilungen, Volume 48, Issue 4, November 2025.Abstract We present a workflow for the inverse design of architected materials with targeted effective mechanical properties. The approach leverages a low‐dimensional descriptor space to represent the topology and morphology of complex mesostructures, enabling efficient navigation within the design space.
Alexandra Otto +3 more
wiley +1 more source
Conjugate cone characterization of positive definite and semidefinite matrices [PDF]
, 1984 S.-P. Han, O. L. Mangasarian
openalex +1 more source
Continuous Time Markov Chain for Smartwatch Sensors
Expert Systems, Volume 42, Issue 11, November 2025.ABSTRACT Time‐series forecasting is essential for predicting events in the future and for tracking objects. The conventional recurrent neural network model needs to pad the target with zeros when handling long inputs, resulting in a loss in accuracy.
Iti Chaturvedi +4 more
wiley +1 more source
From ƒ-Divergence to Quantum Quasi-Entropies and Their Use
Entropy, 2010 Csiszár’s ƒ-divergence of two probability distributions was extended to the quantum case by the author in 1985. In the quantum setting, positive semidefinite matrices are in the place of probability distributions and the quantum generalization is called ...
Dénes Petz
doaj +1 more source