Physics-informed machine learning bridges the gap between the high fidelity of mechanistic models and the adaptive insights of artificial intelligence. In chemical reaction network modeling, this ...
A study in the Journal of Cosmology and Astroparticle Physics explores how a machine-learning strategy known as transfer ...
High accuracy electron microscopy simulations required for quantitative crystal structure refinements face a fundamental challenge: while physical interactions are well-described theoretically, ...
Combining concepts from statistical physics with machine learning, researchers at the University of Bayreuth have shown that highly accurate and efficient predictions can now be made as to whether a ...
Physics AI engineering simulation tools reached production at General Motors this week, cutting a two-week aerodynamics cycle ...
Parisa Khodabakhshi is an assistant professor of mechanical engineering and mechanics in Lehigh University’s P.C. Rossin College of Engineering and Applied Science. Prior to joining the Lehigh faculty ...
Space weather forecasting remains a major challenge in heliophysics, as geomagnetic storms continue to pose significant risks to satellite operations, power ...
Understanding and predicting complex physical systems remain significant challenges in scientific research and engineering. Machine learning models, while powerful, often fail to follow the ...
AI has started to emerge as one of the most effective technologies being used in cosmology lately. The power of machine ...
Machine learning (ML) is reshaping pipeline integrity management (PIM) from physics-based to data-driven paradigms. This ...
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