Results 91 to 100 of about 681,860 (297)

Nordgren PINNs to VQE: Advancing Hydraulic Fracturing Simulations in Shale Reservoirs

International Journal for Numerical and Analytical Methods in Geomechanics, EarlyView.
ABSTRACT This study advances hydraulic fracturing simulations in shale reservoirs using two computational paradigms, Physics‐Informed Neural Networks (PINNs) and the Variational Quantum Eigensolver (VQE). PINNs were employed to solve Nordgren's equation, which governs fracture width evolution, by embedding physical laws into the neural network ...
Dennis Delali Kwesi Wayo, Sonny Irawan, Mohd Zulkifli Bin Mohamad Noor, Mudasar Zafar, Shynar Juziyeva, Camila Martins Saporetti, Leonardo Goliatt, Randy Hazlett   +7 more
wiley   +1 more source

A Family of Newton and Quasi-Newton Methods for Power Flow Analysis in Bipolar Direct Current Networks with Constant Power Loads

Mathematical and Computational Applications
This paper presents a comprehensive study on the formulation and solution of the power flow problem in bipolar direct current (DC) distribution networks with unbalanced constant power loads.
Oscar Danilo Montoya, Juan Diego Pulgarín Rivera, Luis Fernando Grisales-Noreña, Walter Gil-González, Fabio Andrade-Rengifo   +4 more
doaj   +1 more source

A Geometrically Regularized Gradient‐Damage Model With Orthogonality‐Based Energy Split for Dynamic Anisotropic Compression‐Shear Fracture

International Journal for Numerical and Analytical Methods in Geomechanics, EarlyView.
ABSTRACT This study proposes a novel geometrically regularized gradient‐damage model for simulating dynamic mixed‐mode fracture in orthotropic materials with tension–compression asymmetry. In this model, a thermodynamic framework is formulated by incorporating damage dissipation into internal energy evolution, from which the constitutive relation and ...
Hui Li, Shanyong Wang
wiley   +1 more source

Predicting the Longitudinal Dispersion Coefficient in Natural Streams Using Developed Artificial Neural Network Model [PDF]

آب و فاضلاب, 2011
The main objective of the present work is to predict the longitudinal dispersion coefficient in natural streams using a neural network (NN) model which was developed based on Quasi-Newton training functions.
Roohollah Noori, Abdulreza Karbassi, Hamid Mehdizadeh   +2 more
doaj  

Sonified Signals From a Compact FT‐ICR Instrument: A Feasibility Study. I—Data Mapping to an Equal‐Tempered Chromatic Scale

Rapid Communications in Mass Spectrometry, EarlyView.
ABSTRACT Rationale Ions trapped within a Penning cell (ICR) travel periodic orbits whose frequencies are dependent on their mass‐to‐charge ratio and the value of the magnetic field passing through the trap. Fourier transformation (FT‐ICR) decomposes the signal induced in the detection circuit by the rotation of the ions in the cell after the ...
Patrick Arpino, Michel Heninger
wiley   +1 more source

A quasi-Newton proximal splitting method

, 2012
A new result in convex analysis on the calculation of proximity operators in certain scaled norms is derived. We describe efficient implementations of the proximity calculation for a useful class of functions; the implementations exploit the piece-wise linear nature of the dual problem.
Becker, Stephen, Fadili, Jalal M.
openaire   +4 more sources

Quasi-Newton Methods With Derivatives

, 1995
When the Jacobian of a nonlinear system of equations is fully available, the main drawback for the application of Newton's method is the linear algebra work associated with its basic iteration.
J.M. Martínez
core  

Diagonal quasi-Newton updating formula using log-determinant norm [PDF]

, 2015
Quasi-Newton method has been widely used in solving unconstrained optimization problems. The popularity of this method is due to the fact that only the gradient of the objective function is required at each iterate. Since second derivatives (Hessian) are
Siti Nur Iqmal Ibrahim, Sim, Hong Seng, Hong Seng Sim, Chuei Yee Chen, Ibrahim, Siti Nur Iqmal, Wah June Leong, Leong, Wah June, Chen, Chuei Yee   +7 more
core   +1 more source

Machine learning‐driven advances in carbon‐based quantum dots: Opportunities accompanied by challenges

Responsive Materials, EarlyView.
Machine learning provides a unifying framework to connect structure, fluorescence properties, and applications of carbon‐based quantum dots. This review highlights how data‐driven strategies enable fluorescence regulation, reveal underlying mechanisms, and accelerate the rational design of functional carbon dots.
Liangfeng Chen, Siwei Yang, Xiangqian Cui, Ziheng Liu, Hang Wang, Yongqiang Li, Peng He, Miao Wang, Guqiao Ding   +8 more
wiley   +1 more source

Key Technical Fields and Future Outlooks of Space Manipulators: A Survey

SmartBot, EarlyView.
This paper systematically reviews the technological development of space manipulators, emphasizing the unique challenges posed by space environments. It examines four areas: structural design, modeling, planning, and control, while introducing typical ground test platforms.
Gang Chen, Zeyuan Huang, Ying Yan, Xiaokang An, Zhikai Zhao, Jirong Lin, Xiangrong Zhao, Xianyuan Gao, Junyi Zong, Xuewen Jia, Zhipeng Yao, Jianliang Liao, Ning Ji   +12 more
wiley   +1 more source