In this paper, three state-of-the-art deep learning uncertainty quantification (UQ) methods–
Flipout probabilistic convolutional neural network (CNN), deep ensemble probabilistic CNN …

When attempting to develop aerodynamic models for extreme flight conditions, including
high angle of attack, high rotational rates, and tumbling motion, many classical methods …

This study aims to comprehensively investigate the deep ensemble approach, an
approximate Bayesian inference, in the multi-output regression task for predicting the …

Effective verification and validation techniques for modern scientific machine learning
workflows are challenging to devise. Statistical methods are abundant and easily deployed …

In recent years, neural networks (NNs) have become increasingly popular for surrogate
modeling tasks in mechanics and materials modeling applications. While traditional NNs are …

Forecasting dynamical systems is of importance to numerous real-world applications. When
possible, dynamical systems forecasts are constructed based on first-principles-based …

In the past decade, machine learning has experienced exponential growth, propelled by
abundant datasets, algorithmic advancements, and increased computational power …