Results 71 to 80 of about 10,435 (267)
Trust‐region filter algorithms utilizing Hessian information for gray‐box optimization
AIChE Journal, EarlyView.Abstract Optimizing industrial processes often involves gray‐box models that couple algebraic glass‐box equations with black‐box components lacking analytic derivatives. Such systems challenge derivative‐based solvers. The classical trust‐region filter (TRF) algorithm provides a robust framework but requires extensive parameter tuning and numerous ...
Gul Hameed +4 more
wiley +1 more source
Coordination of Bus Departures by Mathematical Programming
Communications, 2013 This paper deals with coordination of bus arrivals. A quality criterion of this problem is to minimize waiting time of passengers, to make public transport more attractive. One possibility for solving is to increase the number of arrivals with associated
Lydia Gabrisova, Petr Kozel
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A trust‐region funnel algorithm for gray‐box optimization
AIChE Journal, EarlyView.Abstract Gray‐box optimization, where parts of optimization problems are represented by algebraic models while others are treated as black‐box models lacking analytic derivatives, remains a challenge. Trust‐region (TR) methods provide a robust framework for gray‐box problems through local reduced models (RMs) for black‐box components, but they are ...
Gul Hameed +4 more
wiley +1 more source
A simultaneous trajectory generation method for quadcopter intercepting ground mobile vehicle
International Journal of Advanced Robotic Systems, 2017 This article proposes a trajectory generator for quadcopter to intercept moving ground vehicle. For this air–ground interaction problem, we formulate the trajectory generation problem as quadratic dynamic programming in a moving-horizon scheme based on ...
Jiangcheng Zhu, Jun Zhu, Chao Xu
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Advanced Intelligent Discovery, EarlyView.To integrate surface analysis into materials discovery workflows, Gaussian process regression is used to accurately predict surface compositions from rapidly acquired volume composition data (obtained by energy‐dispersive X‐ray spectroscopy), drastically reducing the number of required surface measurements on thin‐film materials libraries.
Felix Thelen +2 more
wiley +1 more source
Advanced Intelligent Discovery, EarlyView.A physics‐guided machine learning framework estimates Young's modulus in multilayered multimaterial hyperelastic cylinders using contact mechanics. A semiempirical stiffness law is embedded into a custom neural network, ensuring physically consistent predictions. Validation against experimental and numerical data on C.
Christoforos Rekatsinas +4 more
wiley +1 more source
On solving matrix quadratic programming problems
Journal of Mathematical Analysis and Applications, 1991 The scalar quadratic programming problem is straightforwardly generalized to the matrix quadratic programming problem, and a solution methodology for solving the matrix quadratic programming problem is developed. The methodology is illustrated by simple numerical examples.
openaire +1 more source
Data‐Guided Photocatalysis: Supervised Machine Learning in Water Splitting and CO2 Conversion
Advanced Intelligent Discovery, EarlyView.This review highlights recent advances in supervised machine learning (ML) for photocatalysis, emphasizing methods to optimize photocatalyst properties and design materials for solar‐driven water splitting and CO2 reduction. Key applications, challenges, and future directions are discussed, offering a practical framework for integrating ML into the ...
Paul Rossener Regonia +1 more
wiley +1 more source
On the Solution of a Nonlinear Semidefinite Program Arising in Discrete-Time Feedback Control Design
Journal of Applied Mathematics, 2014 A sequential quadratic programming method with line search is analyzed and studied for finding the local solution of a nonlinear semidefinite programming problem resulting from the discrete-time output feedback problem.
El-Sayed M. E. Mostafa
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Advanced Intelligent Discovery, EarlyView.Factorization machine with iterative quantum reverse annealing (FMIRA) leverages quantum reverse annealing to perform batch black‐box optimization. Factorization machine with quantum annealing (FMQA) is a widely used python package for solving black‐box optimization problems using D‐Wave quantum annealers.
Andrejs Tučs, Ryo Tamura, Koji Tsuda
wiley +1 more source