The computerized simulations of physical and socio-economic systems have proliferated in
the past decade, at the same time, the capability to develop high-fidelity system predictive …

Condition monitoring plays a vital role in ensuring the reliability and optimal performance of
various engineering systems. Traditional methods for condition monitoring rely on physics …

The recent explosion of machine learning (ML) and artificial intelligence (AI) shows great
potential in the breakthrough of metal additive manufacturing (AM) process modeling, which …

Monitoring the health of lithium-ion batteries' internal components as they age is crucial for
optimizing cell design and usage control strategies. However, quantifying component-level …

Gaussian process regression is a popular Bayesian framework for surrogate modeling of
expensive data sources. As part of a broader effort in scientific machine learning, many …

For complex flow problems such as wall-bounded turbulence with multi-scale and strongly
nonlinear characteristics, the non-orthogonal and non-uniform meshes commonly used in …

High-dimensional optimization is a critical challenge for operating large-scale scientific
facilities. We apply a physics-informed Gaussian process (GP) optimizer to tune a complex …

The monitoring process for complex infrastructure requires collecting various data sources
with varying time scales, resolutions, and levels of abstraction. These data sources include …

While multifidelity modeling provides a cost-effective way to conduct uncertainty
quantification with computationally expensive models, much greater efficiency can be …

Estimating uncertainty in flood model predictions is important for many applications,
including risk assessment and flood forecasting. We focus on uncertainty in physics‐based …