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 …

Advancements in computing power have recently made it possible to utilize machine
learning and deep learning to push scientific computing forward in a range of disciplines …

When neural networks are applied to solve complex engineering problems, the lack of
training data can make the predictions of the surrogate inaccurate. Recently, physics …

We propose a novel method for solving partial differential equations using multi-fidelity
physics-informed generative adversarial networks. Our approach incorporates physics …

Multi-fidelity surrogate (MFS) models have garnered significant attention in the field of
engineering optimization due to their ability to attain the desired accuracy at a reduced cost …

Despite their effectiveness in modeling complex phenomena, the adoption of machine
learning (ML) methods in computational mechanics has been hindered by the lack of …

Auxetic metamaterials have been applied in many domains due to their unique auxetic
behavior, tunable local kinematics, and morphological intelligence. However, the classic …

Physics-informed neural networks (PINNs) are a novel approach to solving partial
differential equations (PDEs) through deep learning. They offer a unified manner for solving …

Human intention prediction plays a critical role in human–robot collaboration, as it helps
robots improve efficiency and safety by accurately anticipating human intentions and …

Data sparsity is the main barrier to apply deep neural networks to solve complex scientific
and engineering problems, where it is expensive to obtain a large amount of high-fidelity …