domaininformed
Domaininformed refers to an approach in artificial intelligence and data science that emphasizes incorporating explicit domain knowledge into the design, training, and deployment of models and systems. It contrasts with purely data-driven methods by leveraging information about the real-world context, constraints, and processes that govern the task at hand.
Techniques commonly associated with domaininformed methods include embedding constraints into models or loss functions, using ontologies
Applications span fields such as medicine, where clinical guidelines constrain predictions; geosciences and environmental science, where
Benefits include improved data efficiency, improved interpretability, and adherence to real-world constraints. Challenges include integrating heterogeneous
See also domain knowledge, physics-informed neural networks, knowledge graphs, constrained optimization, and explainable AI.