Results 51 to 60 of about 1,963,970 (223)

Modeling proppant transport and settling in a 3D propagating fracture

Deep Underground Science and Engineering, EarlyView.
A versatile multiphysics tool is presented for simulating the complex processes involved in hydraulic fracturing treatments. The model couples fracture opening and propagation, variable‐density slurry flow, and proppant particle transport within the multiphysics object‐oriented simulation environment framework, based on two‐phase mixture equations and ...
Robert Egert, Aimé Fournier, Wencheng Jin   +2 more
wiley   +1 more source

An approximate ADMM for solving linearly constrained nonsmooth optimization problems with two blocks of variables

, 2019
Nonsmooth convex optimization problems with two blocks of variables subject to linear constraints are considered. A new version of the alternating direction method of multipliers is developed for solving these problems. In this method the subproblems are
Wu, Zhiyou, Bai, Fusheng, Fusheng Bai, Adil M. Bagirov, Zhiyou Wu, Bagirov, Adil, Taheri, Sona, Sona Taheri   +7 more
core   +1 more source

On Decentralized Nonsmooth Optimization [PDF]

, 2023
In decentralized optimization, several nodes connected by a network collaboratively minimize some objective function. For minimization of Lipschitz functions lower bounds are known along with optimal algorithms.
Rogozin, Alexander, Chezhegov, Savelii, Gasnikov, Alexander   +2 more
core   +1 more source

MULTISYMPLECTIC VARIATIONAL INTEGRATORS FOR NONSMOOTH LAGRANGIAN CONTINUUM MECHANICS

Forum of Mathematics, Sigma, 2016
This paper develops the theory of multisymplectic variational integrators for nonsmooth continuum mechanics with constraints. Typical problems are the impact of an elastic body on a rigid plate or the collision of two elastic bodies.
FRANÇOIS DEMOURES, FRANÇOIS GAY-BALMAZ, TUDOR S. RATIU   +2 more
doaj   +1 more source

Machine Learning Modeling of Drill Cuttings Transport Based on Computational Fluid Dynamics Simulation Data

Energy Science &Engineering, EarlyView.
Machine learning surrogates accurately predict drill cuttings transport from computational fluid dynamics data, with Reynolds number dominating system behavior. While tree‐based models excel in accuracy, Gaussian process regression provides physically consistent, smooth predictions essential for reliable drilling simulations.
Kathrin Skinder, Mostafa M. Abdelhafiz, Khizar Shahid, Gunther Brenner   +3 more
wiley   +1 more source

Codifferential method for minimizing nonsmooth DC functions

, 2011
Nonsmooth optimization, Nonconvex optimization, DC functions, Sub-differential, Codifferential, C61,
A. Bagirov, Ugon, Julien, J. Ugon, Bagirov, Adil   +3 more
core   +1 more source

Introduction

, 2020
Nonsmooth optimization (NSO) is among the most challenging tasks in the field of mathematical programming. It addresses optimization problems where objective and/or constraint functions have discontinuous gradients.
Manlio Gaudioso, Napsu Karmitsa, Adil M. Bagirov, Mäkelä, Marko, Gaudioso, Manlio, Karmitsa, Napsu, Marko M. Mäkelä, Sona Taheri, Bagirov, Adil, Taheri, Sona   +9 more
core   +1 more source

Unsupervised and supervised data classification via nonsmooth and global optimization [PDF]

, 2003
Clustering, classification, cluster function, nonsmooth optimization, global optimization, 65K05, 90C26, 90C30, 90C90,
A. Bagirov, J. Yearwood, Rubinov, Alex, Yearwood, John, N. Soukhoroukova, A. Rubinov, Bagirov, Adil, Sukhorukova, Nadezda   +7 more
core   +1 more source

Neural Network‐Based Runtime Monitoring and Control for Unknown Nonlinear Systems

International Journal of Robust and Nonlinear Control, EarlyView.
ABSTRACT Real‐time system monitoring and stabilization become especially challenging when the system dynamics are unknown. This article introduces a novel design for monitoring and stabilizing unknown nonlinear systems with measurement noise. The design utilizes a generic modelling framework by decomposing the system into a known tunable linear ...
Jianglin Lan, Xianxian Zhao, Ron Patton
wiley   +1 more source

Substantiation of the backpropagation technique via the Hamilton—Pontryagin formalism for training nonconvex nonsmooth neural networks

Доповiдi Нацiональної академiї наук України
The paper observes the similarity between the stochastic optimal control over discrete dynamical systems and the lear ning multilayer neural networks. It focuses on contemporary deep networks with nonconvex nonsmooth loss and activation functions.
V.I. Norkin
doaj   +1 more source