Abstract Machine learning (ML) has recently enabled many modeling tasks in design,
manufacturing, and condition monitoring due to its unparalleled learning ability using …

We present a simple and effective approach for posterior uncertainty quantification in deep
operator networks (DeepONets); an emerging paradigm for supervised learning in function …

Since the early 2010s, after decades of premature excitement and disillusionment, the field
of artificial intelligence (AI) is experiencing exponential growth, with massive real-world …

Metal additive manufacturing (AM) works on the principle of consolidating feedstock material
in layers towards the fabrication of complex objects through localized melting and …

The problem of the efficient design of material microstructures exhibiting desired properties
spans a variety of engineering and science applications. The ability to rapidly generate …

Reliability analysis is a core element in engineering design and can be performed with
physical models (limit-state functions). Reliability analysis becomes computationally …

A multi-fidelity (MF) surrogate involving Gaussian processes (GPs) is used for designing
temporal process maps in laser directed energy deposition (L-DED) additive manufacturing …

Science and Engineering applications are typically associated with expensive optimization
problem to identify optimal design solutions and states of the system of interest. Bayesian …

The requirements of future aeroengines challenge turbomachinery designs to be quieter,
greener, and more efficient; furthermore, they must be developed at considerably reduced …

Abstract Machine learning is gaining prominence in mechanical design, offering cost-
effective surrogate models to replace computationally expensive models. Nevertheless …