Abstract: Synchronous machines are fundamental components. Accurate modelling of nonlinear magnetic saturation characteristics is essential. Traditional models often rely on computationally intensive ...
The semiconductor industry is entering an era of unprecedented complexity, driven by advanced architectures such as Gate-All-Around (GAA) transistors, wide-bandgap materials like GaN and SiC, and ...
Machine learning is transforming many scientific fields, including computational materials science. For about two decades, scientists have been using it to make accurate yet inexpensive calculations ...
The simulation tracks a satellite's surface and internal temperature changes as it orbits Earth, considering varying thermal inputs from sunlight and Earth's shadow. Monte Carlo radiation modeling ...
Moog uses advanced modeling and simulation tools—such as Simulink, MATLAB and its proprietary MAST library—to design and optimize high-performance motion control systems. Real-world applications ...
Mathematical optimization is a fundamental aspect of machine learning (ML). An ML task can be conceptualized as optimizing a specific objective using the training dataset to discern patterns and to ...
As semiconductor technologies advance, device structures are becoming increasingly complex. New materials and architectures introduce intricate physical effects requiring accurate modeling to ensure ...
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