Scientific reliability of physics-informed foundation models for engineering: a critical review, evidence-based assessment framework, and future research roadmap
Sep 2026· Machine Learning for Computational Science and Engineering· Vol 2· 0 citations· 76 references
TL;DR
Overall, SRAF provides an evidence-driven basis for developing engineering AI systems that are physically consistent, verifiable, uncertainty-aware, reproducible, computationally efficient, and suitable for industrial deployment.
Accurate prediction of creep degradation across multiple high-temperature alloy grades remains a significant challenge in structural integrity assessment. Classical empirical and mechanistic models are typically restricted to estimating total creep rupture life and are often material-specific, limiting their generaliza...
H. Bhardwaj, Mukul Shukla· Journal of the Brazilian Soc...· 0 citations
The integration of physics-based modeling and data-driven prediction is creating new opportunities for predictive design, optimization, and the deployment of digital twins in advanced manufacturing systems. In compliant mechanisms, particularly double-bridge configurations used in precision positioning and surface en...
V. Kolate, P. D. Darade, Suhas P. Deshmukh· Frontiers of Mechanical Engi...· 0 citations
Generative and predictive artificial intelligence models are increasingly used to generate geometry and to predict physical fields and scalar quantities in engineering design and simulation. Yet these models are typically evaluated in isolation, on academic datasets at unconstrained scales, with inconsistent metrics an...
Predictive monitoring of fluid-machinery assets is hindered by nonlinear dynamics, sparse fault data, and distribution shifts between simulation and field operation. This survey develops a lifecycle-oriented taxonomy and maps evidence across 127 screened records, including 77 studies retained for tiered synthesis. The...
Gerald Canaan Sohn, Jun-Feng Bao, Fang-Hua Ning et al.· IEEE Access· 0 citations
The external load factor (χ) is a key parameter affecting the performance and reliability of bolted joints. This study presents an integrated framework combining experimental measurement, physics-informed surrogate modeling, and reliability-based robust design optimization (RBRDO) to analyze and optimize χ under uncert...
Van Thuy Tran· Advances in Science and Tech...· 0 citations
The increasing demand for energy-efficient and high-performance engineering systems has intensified the need for lightweight mechanical components with high stiffness, strength, and reliability. Topology optimization (TO) enables efficient material distribution within a prescribed design domain, but conventional method...
A. Nega, Atalay Bayable Tiruneh, Teshager Awoke Yeshiwas et al.· Advances in Mechanical Engin...· 0 citations
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