Data assimilation (DA) and uncertainty quantification (UQ) are extensively used in analysing
and reducing error propagation in high-dimensional spatial-temporal dynamics. Typical …

Abstract Machine learning (ML) offers a variety of techniques to understand many complex
problems in different fields. The field of heat transfer, and thermal systems in general, are …

Designers increasingly rely on parametric design studies to explore and improve structural
concepts based on quantifiable metrics, generally either by generating design variations …

This paper introduces a novel surrogate model for two-dimensional adaptive steady-state
thermal convection fields based on deep learning technology. The proposed model aims to …

This paper presents a novel deep learning-based surrogate model for steady-state natural
convection problem with variable geometry. Traditional deep learning based surrogate …

Abstract Machine Learning (ML) algorithms are emerging in various industries as a powerful
complement/alternative to traditional data regression methods. A major reason is that, unlike …

Recently, physics-driven deep learning methods have shown particular promise for the
prediction of physical fields, especially to reduce the dependency on large amounts of pre …

In design for forming, it is becoming increasingly significant to develop surrogate models of
high-fidelity finite element analysis (FEA) simulations of forming processes to achieve …

The complexity of real-world geophysical systems is often compounded by the fact that the
observed measurements depend on hidden variables. These latent variables include …

Conventional numerical heat conduction solvers are exceedingly computationally expensive
and memory demanding. Recent advances in deep learning have witnessed its extensive …