Copper foam reinforced polymer-based phase change material composites for more efficient thermal management of lithium-ion batteries
C Hu, Y Jiang, S Chen, L Wang, H Li, Y Li… - Journal of Energy …, 2023 - Elsevier
In this study, copper foam reinforced polymer-based composite phase change materials
(CPCM) were prepared to solve the problems of low thermal conductivity, melting leakage …
(CPCM) were prepared to solve the problems of low thermal conductivity, melting leakage …
Solution multiplicity and effects of data and eddy viscosity on Navier-Stokes solutions inferred by physics-informed neural networks
Physics-informed neural networks (PINNs) have emerged as a new simulation paradigm for
fluid flows and are especially effective for inverse and hybrid problems. However, vanilla …
fluid flows and are especially effective for inverse and hybrid problems. However, vanilla …
Physics-informed neural networks for incompressible flows with moving boundaries
Physics-informed neural networks (PINNs) employed in fluid mechanics deal primarily with
stationary boundaries. This hinders the capability to address a wide range of flow problems …
stationary boundaries. This hinders the capability to address a wide range of flow problems …
A comprehensive and FAIR comparison between MLP and KAN representations for differential equations and operator networks
Kolmogorov-Arnold Networks (KANs) were recently introduced as an alternative
representation model to MLP. Herein, we employ KANs to construct physics-informed …
representation model to MLP. Herein, we employ KANs to construct physics-informed …
Data-driven approach augmented by attention mechanism in critical and boiling thermophysical properties prediction of fluorine/chlorine-based refrigerants
Refrigerants are ubiquitous in modern society, indispensable for various applications
ranging from air conditioners and refrigerators to spacecraft and hospitals. Improving …
ranging from air conditioners and refrigerators to spacecraft and hospitals. Improving …
Assessing the enhancements in thermal performance of two-phase thermosyphon loops through riser wettability optimization
The behavior of vapor-liquid two-phase transport within the riser of the two-phase
thermosyphon loop significantly impacts the heat transfer process from the evaporation to …
thermosyphon loop significantly impacts the heat transfer process from the evaporation to …
Modeling two-phase flows with complicated interface evolution using parallel physics-informed neural networks
R Qiu, H Dong, J Wang, C Fan, Y Wang - Physics of Fluids, 2024 - pubs.aip.org
The physics-informed neural networks (PINNs) have shown great potential in solving a
variety of high-dimensional partial differential equations (PDEs), but the complexity of a …
variety of high-dimensional partial differential equations (PDEs), but the complexity of a …
Operator Learning Enhanced Physics-informed Neural Networks for Solving Partial Differential Equations Characterized by Sharp Solutions
Physics-informed Neural Networks (PINNs) have been shown as a promising approach for
solving both forward and inverse problems of partial differential equations (PDEs) …
solving both forward and inverse problems of partial differential equations (PDEs) …
[HTML][HTML] Physics Guided Neural Networks with Knowledge Graph
Over the past few decades, machine learning (ML) has demonstrated significant
advancements in all areas of human existence. Machine learning and deep learning models …
advancements in all areas of human existence. Machine learning and deep learning models …
Assessing physics-informed neural network performance with sparse noisy velocity data
A Satyadharma, MJ Chern, HC Kan, H Harinaldi… - Physics of …, 2024 - pubs.aip.org
The utilization of data in physics-informed neural network (PINN) may be considered as a
necessity as it allows the simulation of more complex cases with a significantly lower …
necessity as it allows the simulation of more complex cases with a significantly lower …