This paper applies deep reinforcement learning (DRL) on the synthetic jet control of flows
over an NACA (National Advisory Committee for Aeronautics) 0012 airfoil under weak …

In this paper, we proposed an innovative Bayesian optimization (BO) coupled with deep
learning for rapid airfoil shape optimization to maximize aerodynamic performance of …

This paper introduces a novel surrogate model for two-dimensional adaptive steady-state
thermal convection fields based on deep learning technology. The proposed model aims to …

In this paper, we propose and investigate a deep convolutional neural network-based
surrogate model for fast prediction of heat transfer of nanofluid in absorbent tubes with fins …

In this study, we develop an approach based on deep learning and the Bayesian method for
fast optimization of the thermal placement of the multichip modules (MCMs). Specifically, a …

To alleviate the computational burden associated with the computational fluid dynamics
(CFD) simulation stage and improve aerodynamic optimization efficiency, this work develops …

In the interdisciplinary field of data-driven models and computational fluid mechanics, the
reduced-order model for flow field prediction is mainly constructed by a convolutional neural …

The present study introduces a novel optimization framework that combines a Graph
Convolutional Neural Network surrogate model with Genetic Algorithms (GCN-GA). This …

In this paper, deep reinforcement learning (DRL) is applied on forced convection control of
conjugate heat transfer systems governed by the coupled Navier-Stokes and heat transport …

The stochastic procedures ANNs-LMB are provided with three categories of sample
statistics, testing, training and verification. The nonlinear mass and heat transfer of …