Data-Centric Engineering: Integrating Simulation, Machine Learning, and Statistics

Abstract

Data-centric engineering (DCE) represents a paradigm shift in modern engineering by merging traditional physics-based simulation approaches with machine learning (ML) and statistical methods. As industries face increasing demands for system complexity, efficiency, and reliability, DCE offers a robust framework to model, predict, and optimize engineering processes and products. This article delves deep into the confluence of simulation, ML, and statistics, showcasing how they synergize to improve engineering workflows. By leveraging high-fidelity simulations, advanced ML algorithms, and statistical inference, DCE enables real-time decision-making, anomaly detection, design optimization, and predictive maintenance. In this paper, we explore the historical evolution of DCE, current applications, and its transformative potential across aerospace, automotive, civil infrastructure, and manufacturing domains. The integration of multi-source data, model uncertainty quantification, and digital twins forms the core of our discussion. We present detailed methodologies for hybrid modeling approaches and data fusion techniques and provide experimental results that validate the efficiency of DCE in improving performance metrics. Case studies demonstrate significant improvements in product life cycles, resource allocation, and safety standards. The findings emphasize that DCE is not just a technological advancement but a foundational strategy for next-generation engineering solutions.