Results 91 to 100 of about 22,074 (263)

Nonexplosion criteria for relativistic diffusions

, 2012
Some general Lorentz covariant operators, associated to the so-called \Theta (or \Xi)-relativistic diffusions and making sense in any Lorentzian manifold, have been introduced by Franchi and Le Jan [Comm. Pure Appl. Math.
Bailleul, Ismaël, Franchi, Jacques
core   +1 more source

Deep Learning Prediction of Surface Roughness in Multi‐Stage Microneedle Fabrication: A Long Short‐Term Memory‐Recurrent Neural Network Approach

Advanced Intelligent Discovery, EarlyView.
A sequential deep learning framework is developed to model surface roughness progression in multi‐stage microneedle fabrication. Using real‐world experimental data from 3D printing, molding, and casting stages, an long short‐term memory‐based recurrent neural network captures the cumulative influence of geometric parameters and intermediate outputs ...
Abdollah Ahmadpour, Sara Soltanabadi Farshi, Tuanna Ozcan, Amir Mohammad Ketabchi, Alper Kiraz, Savas Tasoglu   +5 more
wiley   +1 more source

Ginzburg-Landau theory of the liquid-solid interface and nucleation for hard-spheres

, 2006
The Ginzburg-Landau free energy functional for hard-spheres is constructed using the Fundamental Measure Theory approach to Density Functional Theory as a starting point.
Lutsko, James F.
core   +1 more source

Identification of Exhaled Volatile Organic Compounds Biomarkers for Lung Cancer Under Data‐Limited Conditions Using Data Augmentation and Multi‐View Feature Selection

Advanced Intelligent Discovery, EarlyView.
This work introduces a novel framework for identifying non‐small cell lung cancer biomarkers from hundreds of volatile organic compounds in breath, analyzed via gas chromatography‐mass spectrometry. This method integrates generative data augmentation and multi‐view feature selection, providing a stable and accurate solution for biomarker discovery in ...
Guancheng Ren, Bohao Liu, Zhiyuan Qu, Xue Jiang, Heng Zhao, Danyao Qu, Ruizhi Ning, Chen Su, Taoping Liu, Guangjian Zhang, Weiwei Wu   +10 more
wiley   +1 more source

Recollapse of the closed Tolman spacetimes

, 1993
The closed-universe recollapse conjecture is studied for the spherically symmetric spacetimes. It is proven that there exists an upper bound to the lengths of timelike curves in any Tolman spacetime that possesses $S^3$ Cauchy surfaces and whose energy ...
C. Hellaby, C. W. Misner, C. W. Misner, D. M. Witt, G. A. Burnett, Gregory A. Burnett, J. D. Barrow, J. D. Barrow, J. D. Barrow, J. E. Marsden, L. D. Landau, R. C. Tolman, R. M. Wald, S. W. Hawking, W. B. Bonnor, X.-F. Lin, X.-F. Lin, Y. B. Zel'dovich, Y. Choquet-Bruhat   +18 more
core   +1 more source

A Physics Constrained Machine Learning Pipeline for Young's Modulus Prediction in Multimaterial Hyperelastic Cylinders Guided by Contact Mechanics

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, Panagiotis Krokidas, Vasileios Vavourakis, Clara Essmann, George Giannakopoulos   +4 more
wiley   +1 more source

Computational Method for Phase Space Transport with Applications to Lobe Dynamics and Rate of Escape

, 2017
Lobe dynamics and escape from a potential well are general frameworks introduced to study phase space transport in chaotic dynamical systems. While the former approach studies how regions of phase space are transported by reducing the flow to a two ...
A.M. Mancho, B. Maelfeyt, B. Sandstede, C. C. Martens, C. Jaffé, C. Jaffé, D. Babyuk, D. Beigie, D. Beigie, D. G. Dritschel, E. E. Zotos, E. E. Zotos, F. A. McRobie, F. Lekien, Francois Lekien, H. Aref, H. Aref, J. Duan, J. O’Rourke, J.M.T. Thompson, K. A. Mitchell, K. A. Mitchell, K. A. Mitchell, M. Dellnitz, M. I. Shamos, M. Toda, M.A. Sepulveda, R. Barrio, R. E. Gillilan, R. S. Mackay, S. Balasuriya, S. Balasuriya, S. Balasuriya, S. Wiggins, S. Wiggins, Sh. D. Ross, Sh. Naik, Shane D. Ross, Shibabrat Naik, V. Rom-Kedar, V. Rom-Kedar, V. Rom-Kedar, W. S. Koon, W. S. Koon, W. S. Koon   +44 more
core   +1 more source

Gaussian Process Regression–Neural Network Hybrid with Optimized Redundant Coordinates: A New Simple Yet Potent Tool for Scientist's Machine Learning Toolbox

Advanced Intelligent Discovery, EarlyView.
A machine learning method, opt‐GPRNN, is presented that combines the advantages of neural networks and kernel regressions. It is based on additive GPR in optimized redundant coordinates and allows building a representation of the target with a small number of terms while avoiding overfitting when the number of terms is larger than optimal.
Sergei Manzhos, Manabu Ihara
wiley   +1 more source

Peristaltic flow of magnetized hyperbolic tangent fluid through an asymmetric channel in the presence of homogeneous-heterogeneous reactions

Advances in Mechanical Engineering
The hyperbolic tangent fluid model accurately captures the shear-thinning phenomenon and is widely used in both laboratory experiments and industrial applications.
Mariam Sheikh, Muhammad Rizwan, Jafar Hasnain, Zaheer Abbas, Muhammad Yousuf Rafiq   +4 more
doaj   +1 more source

RAMS: Residual‐Based Adversarial‐Gradient Moving Sample Method for Scientific Machine Learning in Solving Partial Differential Equations

Advanced Intelligent Discovery, EarlyView.
We propose a residual‐based adversarial‐gradient moving sample (RAMS) method for scientific machine learning that treats samples as trainable variables and updates them to maximize the physics residual, thereby effectively concentrating samples in inadequately learned regions.
Weihang Ouyang, Min Zhu, Wei Xiong, Si‐Wei Liu, Lu Lu   +4 more
wiley   +1 more source