Results 181 to 190 of about 381,752 (282)
A symbolic dataset for large language models to solve second kind Fredholm integral equations. [PDF]
Dana Mazraeh H +3 more
europepmc +1 more source
ABSTRACT Hybrid modeling combines first‐principles equations with a data‐driven subcomponent. Training for the data‐driven part is sensitive to measurement noise when training targets are constructed using pointwise time derivatives. Beyond differentiation errors, hybrid models involve solving an inverse problem to estimate the data‐driven term, which ...
Hangjun Cho +4 more
wiley +1 more source
Enhancing bioinformatics engineering by utilizing graph therapeutic properties for clinically approved antitoxin drugs in zoonotic diseases. [PDF]
Imran M, Aqib M, Malik MA, Jutt S.
europepmc +1 more source
Abstract The demand for LiOH is driven by the growth of the electric vehicle industry. Evaporative crystallization of LiOH·H2O is energy intensive, whereas ethanol‐based antisolvent crystallization has emerged as a more sustainable alternative. From a process design perspective, the crystallization yield depends on the ethanol dosage, and thermodynamic
Xiaoqi Xu +3 more
wiley +1 more source
Discontinuity Characterization and Low-Complexity Smoothing in RF-PA Polynomial Piecewise Modeling. [PDF]
Pedrosa C +5 more
europepmc +1 more source
CFD modeling and sensitivity‐guided design of silicon filament CVD reactors
Abstract Filament‐based chemical vapor deposition (CVD) for silicon (Si) coatings is often treated as an adaptation of planar deposition. But this overlooks fundamental shifts in transport phenomena and reaction kinetics. In filament CVD, the filament acts as a substrate, heat source, and flow disruptor simultaneously. In this work, we ask: What really
G. P. Gakis +8 more
wiley +1 more source
Tight Approximation and Kernelization Bounds for Vertex-Disjoint Shortest Paths. [PDF]
Bentert M, Fomin FV, Golovach PA.
europepmc +1 more source
In this study we employed support vector regressor and quantum support vector regressor to predict the hydrogen storage capacity of metal–organic frameworks using structural and physicochemical descriptors. This study presents a comparative analysis of classical support vector regression (SVR) and quantum support vector regression (QSVR) in predicting ...
Chandra Chowdhury
wiley +1 more source
Reducing Complexity in Muscle-Tendon Kinematics Parameterization Improves Convergence Speed in Musculoskeletal Simulations. [PDF]
Harba M, Badia J, Serrancolí G.
europepmc +1 more source
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 +5 more
wiley +1 more source

