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Scientific Machine Learning (SciML) integrates machine learning with scientific computing to improve predictive modeling, optimization, and decision-making in complex systems. Unlike traditional AI, SciML incorporates domain knowledge (e.g., physics, chemistry, and engineering) into learning algorithms, ensuring models are both data-efficient and scientifically interpretable.

The JuliaSim framework provides enterprises with a modern engineering workflow from the ground up, safely incorporating Scientific Machine Learning (SciML) advances into systems level modeling and engineering practice as a productivity accelerator to the practitioners.