Results 221 to 230 of about 134,418 (279)

Cam Design and Pin Defect Detection of Cam Pin Insertion Machine in IGBT Packaging. [PDF]

Micromachines (Basel)
Tian W, Zhang P, Tian M, Chen S, Ji H, Ma B.   +5 more
europepmc   +1 more source

Comparison of DeePMD, MTP, GAP, ACE and MACE Machine‐Learned Potentials for Radiation‐Damage Simulations: A User Perspective

Advanced Intelligent Discovery, EarlyView.
The authors evaluated six machine‐learned interatomic potentials for simulating threshold displacement energies and tritium diffusion in LiAlO2 essential for tritium production. Trained on the same density functional theory data and benchmarked against traditional models for accuracy, stability, displacement energies, and cost, Moment Tensor Potential ...
Ankit Roy, Ram Devanathan, Sarah Allec, Giridhar Nandipati, Andrew M. Casella, David J. Senor, Duane D. Johnson, Ganesh Balasubramanian, Ayoub Soulami   +8 more
wiley   +1 more source

Analytical investigation of soliton propagation in conformable fractional-order transmission line metamaterials. [PDF]

Sci Rep
Almetwally EM, Zayed MA, Abo-Hashem SI, Sadat R, Mabrouk SM, Rashed AS.   +5 more
europepmc   +1 more source

Parametric optimization and constrained optimal control for polynomial dynamical systems

, 2008
openaire   +2 more sources

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

Multiparametric quantification of bacterial cells using digital holographic microscopy. [PDF]

Sci Rep
Cano Á, Sanz-Jiménez A, Malvar O, Ruz JJ, Kosaka PM, García-López S, Calleja M, Tamayo J.   +7 more
europepmc   +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

Application of FT-NIR spectroscopy to the prediction of Chromium contamination in soil by evolutionary chemometrics. [PDF]

PLoS One
Hong S, Liang Z, Chen H, Weng J, Cai K, Wu X.   +5 more
europepmc   +1 more source

Explaining the Origin of Negative Poisson's Ratio in Amorphous Networks With Machine Learning

Advanced Intelligent Discovery, EarlyView.
This review summarizes how machine learning (ML) breaks the “vicious cycle” in designing auxetic amorphous networks. By transitioning from traditional “black‐box” optimization to an interpretable “AI‐Physics” closed‐loop paradigm, ML is shown to not only discover highly optimized structures—such as all‐convex polygon networks—but also unveil hidden ...
Shengyu Lu, Xiangying Shen
wiley   +1 more source

Comparison of neural and traditional techniques for soliton structures and dynamical behavior in a double-chain DNA model. [PDF]

Sci Rep
Majid SZ, Sağlam FNK, Ullah MS.
europepmc   +1 more source