The field of computational materials science has been profoundly transformed by integrating deep learning and other machine learning methodologies. These sophisticated data-driven approaches have ...
Physics-based machine learning unlocks 3D printing potential, thanks to work from Lehigh University's Parisa Khodabakhshi.
Deep Learning with Yacine on MSN
Muon Optimizer for Dense Linear Layers – Newton-Schulz Method with Momentum Explained
Dive deep into the Muon Optimizer and learn how it enhances dense linear layers using the Newton-Schulz method combined with ...
A new method developed at the University of Warwick offers the first simple and predictive way to calculate how irregularly ...
Liu, X. , Mei, R. , Zhao, Z. , Wang, S. and Duan, J. (2025) An Optimized Port Operation Efficiency Prediction Model Based on ...
Abstract: In recent years, the growth of distributed renewable energy has significantly reduced the carbon emissions of the power system. However, due to its volatility, it is difficult to integrate ...
Abstract: Kolmogorov-Arnold Networks (KANs) offer superior fitting ability and interpretability compared to traditional Multi-Layer Perceptrons (MLPs). However, their complex structure and lengthy ...
Advancements in nonlinear optics using 2D materials are transforming photonic devices, offering enhanced performance and ...
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