In the context of the rapid development of computer hardware and the continuous improvement of the artificial intelligence and deep learning theory, aiming at the traditional numerical solution method ...
ABSTRACT: Machine learning (ML) has become an increasingly central component of high-energy physics (HEP), providing computational frameworks to address the growing complexity of theoretical ...
Scientific research has unveiled fascinating insights into how certain aromas trigger powerful emotional and behavioral responses in humans. The phenomenon of scent addiction stems from the direct ...
Abstract: We propose the Physics-Informed Neural Network-driven Sparse Field Discretization method (PINN-SFD), a novel self-supervised, physics-informed deep learning approach for addressing the ...
One of the key steps in developing new materials is “property identification,” which has long relied on massive amounts of experimental data and expensive equipment, limiting research efficiency. A ...
Department of Chemical and Biochemical Engineering, Western University, London, Ontario N6A 5B9, Canada ...
Department of Chemistry and Chemical Engineering, Education and Research Center for Smart Energy and Materials, Inha University, Incheon 22212, Republic of Korea ...
Abstract: In this paper, a method for inferring the motion intentions of a neighboring vehicle ahead of an ego vehicle using a physics-informed deep neural network-based open-set classification ...
Accessing ocean velocity data is critical to improving our understanding of ocean dynamics, which affects our prediction capabilities for a range of services that the ocean provides. Because ocean ...
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