Rethinking machine learning for engineering

Abstract

Machine learning (ML) has driven major advances across engineering domains, from early data-driven approaches to deep learning and, more recently, foundation models. Despite rapidly increasing model capabilities, the practical impact of ML in engineering systems remains limited. We argue that this gap arises from a fundamental misalignment between prevailing ML paradigms and the realities of engineering environments. Modern ML methods, largely developed for data-rich and statistically stable domains, are fundamentally ill-suited for engineering systems characterized by sparse, noisy, and context-dependent data, uncertain ground truth, non-stationary dynamics, strict deployment constraints, and strong physical and domain knowledge requirements. To address this mismatch, we first identify four core challenges: data and benchmark limitations, distribution shift and generalization, resource and deployment constraints, and insufficient integration of domain knowledge. Then we propose a corresponding four-shift framework: (i) from data quantity to engineered data foundations, (ii) from static learning to adaptive and robust learning, (iii) from model scaling to resource-efficient and deployment-aware intelligence, and (iv) from general-purpose models to domain-specific, knowledge-informed foundation models. Building on this framework, we outline four research directions toward Engineering Intelligence: data foundation intelligence, reliable and generalizable intelligence, sustainable and deployable intelligence, and knowledge-centric intelligence. Finally, we introduce a data maturity-deployment scale framework, arguing that engineering AI should be understood not as a search for a single best model, but as an evolving intelligence strategy that co-adapts with system maturity, operational context, and deployment scale.