Recent advances on machine learning for computational fluid dynamics: A survey
This paper explores the recent advancements in enhancing Computational Fluid Dynamics
(CFD) tasks through Machine Learning (ML) techniques. We begin by introducing …
(CFD) tasks through Machine Learning (ML) techniques. We begin by introducing …
Implementation of artificial intelligence in modeling and control of heat pipes: a review
Heat pipe systems have attracted increasing attention recently for application in various heat
transfer-involving systems and processes. One of the obstacles in implementing heat pipes …
transfer-involving systems and processes. One of the obstacles in implementing heat pipes …
[HTML][HTML] Numerical simulation of heat pipes in different applications
Nowadays heat pipes are considered to be popular passive heat transfer technologies due
to their high thermal performance. The heat pipe is a superior heat transfer apparatus in …
to their high thermal performance. The heat pipe is a superior heat transfer apparatus in …
Efficient learning of mesh-based physical simulation with bi-stride multi-scale graph neural network
Learning the long-range interactions on large-scale mesh-based physical systems with flat
Graph Neural Networks (GNNs) and stacking Message Passings (MPs) is challenging due …
Graph Neural Networks (GNNs) and stacking Message Passings (MPs) is challenging due …
Physics-informed regularization for domain-agnostic dynamical system modeling
Learning complex physical dynamics purely from data is challenging due to the intrinsic
properties of systems to be satisfied. Incorporating physics-informed priors, such as in …
properties of systems to be satisfied. Incorporating physics-informed priors, such as in …
On delay partial differential and delay differential thermal models for variable pipe flow
A new formulation of physical thermal models for variable plug flow through a pipe is
proposed. The derived model is based on a commonly used one-dimensional distributed …
proposed. The derived model is based on a commonly used one-dimensional distributed …
Neural Dynamics for Science: The Symbiosis of Deep Graph Learning and Differential Equations
Z Huang - 2024 - escholarship.org
Many scientific problems require a deep understanding of internal structures and complex
dynamics, spanning physical interactions within molecules, brain networks, and beyond …
dynamics, spanning physical interactions within molecules, brain networks, and beyond …