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 ...
Omarkhan Samarkanov, Masoud Riazi School of Mining and Geosciences Presented at: 6th EAGE Global Energy Transition Conference & Exhibition (GET ...
AI has successfully been applied in many areas of science, advancing technologies like weather prediction and protein folding ...
Neural and computational evidence reveals that real-world size is a temporally late, semantically grounded, and hierarchically stable dimension of object representation in both human brains and ...
Artificial intelligence (AI) and machine learning (ML) are revolutionizing the way we understand and predict soil processes. Yet, while data-driven models ...
It shows the schematic of the physics-informed neural network algorithm for pricing European options under the Heston model. ...
Abstract: Traveltime tomography is widely used in seismology to construct accurate long-wavelengthsubsurface velocity models and to investigate the Earth’s internal structure and dynamic processes.
Machine learning techniques that make use of tensor networks could manipulate data more efficiently and help open the black ...
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 ...
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