Application of machine learning and computational methods to scientific data, prediction, modeling, and simulation. Solutions in this category are designed around the structure of scientific problems rather than generic business datasets.
Application of machine learning methods to scientific datasets and problems where domain structure, physical constraints, or scientific interpretation matter.
Development of analytical workflows that use machine learning and AI to identify patterns, classify observations, and extract useful information from scientific datasets.
Development of predictive and computational models for scientific variables, processes, and outcomes.
Integration of AI methods with simulation workflows to accelerate, approximate, analyze, or improve computational experiments.