On top of machine learning (ML) models, uncertainty quantification (UQ) functions as an
essential layer of safety assurance that could lead to more principled decision making by …

Deep learning has been shown to be an effective tool in solving partial differential equations
(PDEs) through physics-informed neural networks (PINNs). PINNs embed the PDE residual …

A multifidelity surrogate (MFS) model is a data fusion method for the enhanced prediction of
less intensively sampled primary variables of interest (ie, high-fidelity (HF) samples) with the …

View Video Presentation: https://doi. org/10.2514/6.2022-2350. vid In this work, the gradient-
enhanced multifidelity neural networks (GEMFNN) algorithm is extended to handle multiple …

Front wings are a key element in the aerodynamic performance of Formula 1 race cars.
Thus, their optimisation makes an important contribution to the performance of cars in races …

A multi-fidelity regression model is proposed for combining multiple datasets with different
fidelities, particularly abundant low-fidelity data and scarce high-fidelity observations. The …

Soil water content (SWC) is a vital variable in the hydrological cycle, while simulation of it
often relies on resolving the soil water flow equation. To cope with the unavailability or poor …

We propose the novel augmented Gaussian random field (AGRF), which is a universal
framework incorporating the data of observable and derivatives of any order. Rigorous …

In this paper, we introduced the gradient-enhanced fractional physics-informed neural
networks (gfPINNs) for solving the forward and inverse problems of the multiterm time …

The timely deployment of vehicle restraint systems is crucial in mitigating the impact of
collisions and protecting occupants in the affected vehicles. The level of protection can be …