Researchers at the University of Bayreuth have developed a method using artificial intelligence that can significantly speed up the calculation of liquid properties. The AI approach predicts the ...
Traditional computational electromagnetics (CEM) methods—such as MoM, FEM, or FDTD—offer high fidelity, but struggle to scale ...
Khalifa University of Science and Technology today announced 270 participants attended the inaugural Winter School on ...
Abstract: The numerical simulation of Homann flow, a classical problem in fluid dynamics involving axisymmetric flow over a solid plate, is essential for understanding various boundary layer phenomena ...
A neural network is a machine learning model originally inspired by how the human brain works (Courtesy: Shutterstock/Jackie Niam) Precision measurements of theoretical parameters are a core element ...
Abstract: Production cost minimization (PCM) problem in power system faces critical challenges in achieving rapid solving. The conventional time-domain decomposition (c–TDD) approach is widely used to ...
Machine learning and neural nets can be pretty handy, and people continue to push the envelope of what they can do both in high end server farms as well as slower systems. At the extreme end of the ...
BingoCGN employs cross-partition message quantization to summarize inter-partition message flow, which eliminates the need for irregular off-chip memory access and utilizes a fine-grained structured ...
1 Automatic Control and Systems Engineering, University of Sheffield, Sheffield, United Kingdom 2 Computer Science, University of Sheffield, Sheffield, United Kingdom In the last decade there has been ...
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