Data-driven machine learning approaches have been proposed to facilitate wireless network optimization by learning latent knowledge from historical optimization instances. However, existing works use ...

A new scientific machine learning framework developed by Professors Horacio D. Espinosa, Sridhar Krishnaswamy, and collaborators accurately predicts and inversely designs the mechanical behavior of ...

The dynamic optimization of large-scale transportation networks presents significant challenges due to their complexity, stochasticity, and the need for real-time decision-making. In conventional ...

Logility, a leader in AI-first supply chain management software, has ushered in a new era of supply chain design with the delivery of Continuous Netwo ...

New research by the University of Amsterdam and Constructor University, Bremen, introduces Neural Flow Diffusion Models (NFDM). This framework enables the forward process to specify and learn latent ...

Therefore, according to the time characteristics of the external deformation of the dam, the NAR dynamic neural network algorithm is applied to the dam safety monitoring. There are many studies on the ...