The present study proposes a two-stage Bi-Fidelity Deep Neural Network surrogate model to
quantify the uncertainty of structural analysis using low-fidelity data samples added to the …

Temperature field prediction is of great importance in the thermal design of systems
engineering, and building a surrogate model is an effective method for the task. Ensuring a …

Abstract Machine learning (ML)–based data-driven approaches have become increasingly
prevalent for predicting structural performance. Because a properly trained ML model can …

Data-driven prediction of laminar flow and turbulent flow in marine and aerospace
engineering has received extensive research and demonstrated its potential in real-time …

Ground settlement prediction during mechanized tunneling is of paramount importance and
remains a challenging research topic. Typically, two paradigms are existing: a physics …

Low order models can be used to accelerate engineering design processes. Ideally, these
surrogates should meet the conflicting requirements of large design space coverage, high …

Abstract Digital Twins (DTs) are widely used for design, manufacturing, prognostics, and
decision support for operations. One critical challenge in optimizing DTs usually involves …

In this study, we develop a novel multi-fidelity deep learning approach that transforms low-
fidelity solution maps into high-fidelity ones by incorporating parametric space information …

Multi-fidelity (MF) methods are gaining popularity for enhancing surrogate modeling and
design optimization by incorporating data from both high-and various low-fidelity (LF) …

Aerodynamic shape design is essential for improving aircraft performance and efficiency.
First, this study introduces a data-driven optimization framework utilizing a multi-fidelity …