A review of physics-informed machine learning in fluid mechanics
Physics-informed machine-learning (PIML) enables the integration of domain knowledge
with machine learning (ML) algorithms, which results in higher data efficiency and more …
with machine learning (ML) algorithms, which results in higher data efficiency and more …
[HTML][HTML] Enhancing property prediction and process optimization in building materials through machine learning: A review
K Stergiou, C Ntakolia, P Varytis, E Koumoulos… - Computational Materials …, 2023 - Elsevier
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 …
highly rigorous approach to assure long-term success. Due to a recent increase in the …
Physics-guided deep learning for dynamical systems: A survey
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 …
Traditional physics-based models are sample efficient, and interpretable but often rely on …
Complex nonlinear dynamics and vibration suppression of conceptual airfoil models: A state-of-the-art overview
During the past few decades, several significant progresses have been made in exploring
complex nonlinear dynamics and vibration suppression of conceptual aeroelastic airfoil …
complex nonlinear dynamics and vibration suppression of conceptual aeroelastic airfoil …
Ultralow Energy Consumption Angstrom-Fluidic Memristor
D Shi, W Wang, Y Liang, L Duan, G Du, Y Xie - Nano Letters, 2023 - ACS Publications
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 …
functions of biological neurons. Here, we report neuromorphic signaling using Angstrom …
Deep-learning-based aerodynamic shape optimization of rotor airfoils to suppress dynamic stall
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 …
airfoils with the aim of suppressing dynamic stall is computationally expensive and …
Reduced-order modeling of fluid flows with transformers
AP Hemmasian, A Barati Farimani - Physics of Fluids, 2023 - pubs.aip.org
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 …
several decades. The huge computational cost of direct numerical simulations has motivated …
Current trends in fluid research in the era of artificial intelligence: a review
F Sofos, C Stavrogiannis, KK Exarchou-Kouveli… - Fluids, 2022 - mdpi.com
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 …
driven by the incorporation of massive amounts of data, either in textual or graphical form …
Recent advances in feature extraction techniques for high-speed flowfields
S Unnikrishnan - Progress in Aerospace Sciences, 2023 - Elsevier
Abstract Space–time scale-resolved diagnostic and computational campaigns routinely
produce high-fidelity multi-disciplinary truth-model quality datasets for complex …
produce high-fidelity multi-disciplinary truth-model quality datasets for complex …
Deep neural network based reduced-order model for fluid–structure interaction system
R Han, Y Wang, W Qian, W Wang, M Zhang… - Physics of Fluids, 2022 - pubs.aip.org
Fluid–structure interaction analysis has high computing costs when using computational
fluid dynamics. These costs become prohibitive when optimizing the fluid–structure …
fluid dynamics. These costs become prohibitive when optimizing the fluid–structure …