Physics-informed machine-learning (PIML) enables the integration of domain knowledge
with machine learning (ML) algorithms, which results in higher data efficiency and more …

Abstract Analysis and design, as the most critical components in material science, require a
highly rigorous approach to assure long-term success. Due to a recent increase in the …

Modeling complex physical dynamics is a fundamental task in science and engineering.
Traditional physics-based models are sample efficient, and interpretable but often rely on …

During the past few decades, several significant progresses have been made in exploring
complex nonlinear dynamics and vibration suppression of conceptual aeroelastic airfoil …

The emergence of nanofluidic memristors has made a giant leap to mimic the neuromorphic
functions of biological neurons. Here, we report neuromorphic signaling using Angstrom …

The use of computational fluid dynamics (CFD) to optimize the aerodynamic shape of rotor
airfoils with the aim of suppressing dynamic stall is computationally expensive and …

Reduced-order modeling (ROM) of fluid flows has been an active area of research for
several decades. The huge computational cost of direct numerical simulations has motivated …

Computational methods in fluid research have been progressing during the past few years,
driven by the incorporation of massive amounts of data, either in textual or graphical form …

Abstract Space–time scale-resolved diagnostic and computational campaigns routinely
produce high-fidelity multi-disciplinary truth-model quality datasets for complex …

Fluid–structure interaction analysis has high computing costs when using computational
fluid dynamics. These costs become prohibitive when optimizing the fluid–structure …