domainneural
Domainneural refers to a class of methods that integrate domain-specific knowledge with neural networks to improve learning in specialized tasks. The term is not universally standardized and can denote a range of techniques that penalize or constrain models according to known rules, or that embed structured domain information within model architectures. In practice, domainneural methods seek to combine data-driven learning with prior knowledge from a domain such as physics, biology, or engineering, enabling models to generalize better from limited data and to produce results that align with established domain constraints.
Common approaches include incorporating domain constraints in loss functions or output spaces; designing architectural inductive biases
Applications are found across fields where domain knowledge is valuable but data may be scarce or costly
See also: neural networks, domain adaptation, physics-informed neural networks, neuro-symbolic AI.