Process engineers are under pressure to optimize production while managing variable feedstock and shifting process conditions. Aspen Hybrid Models™ simplifies the use of machine learning in process ...

This approach combined: (1) supervised machine learning for text classification, (2) comparative topic modeling with both theory-driven and data-driven Latent Dirichlet Allocation (LDA) to identify ...

Already registered? Click here to login now! Join this webinar to see how DriveWorks generates models and employs strategies that make it fast and efficient. You’ll also learn practical SOLIDWORKS ...

Abstract: Synchronous machines are fundamental components. Accurate modelling of nonlinear magnetic saturation characteristics is essential. Traditional models often rely on computationally intensive ...

Claude writes confident MATLAB code, but it sometimes makes up function names that don't exist in R2025b. These skills fix that. They give Claude a quick-reference of tricky APIs, deprecated functions ...

Undiagnosed osteoporosis before spinal surgery increases severe complication risks. This study develops the machine learning-based CT radiomics model to preoperatively screen lumbar osteoporosis. This ...

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 ...

Already registered? Click here to login now. Linear electromagnetic devices — such as linear motors, generators, actuators, and magnetic gears — play a vital role in precision motion control, energy ...