Machine learning in aerodynamic shape optimization
Abstract Machine learning (ML) has been increasingly used to aid aerodynamic shape
optimization (ASO), thanks to the availability of aerodynamic data and continued …
optimization (ASO), thanks to the availability of aerodynamic data and continued …
Applications of machine learning to wind engineering
Advances of the analytical, numerical, experimental and field-measurement approaches in
wind engineering offers unprecedented volume of data that, together with rapidly evolving …
wind engineering offers unprecedented volume of data that, together with rapidly evolving …
A hierarchical framework for improving ride comfort of autonomous vehicles via deep reinforcement learning with external knowledge
Ride comfort plays an important role in determining the public acceptance of autonomous
vehicles (AVs). Many factors, such as road profile, driving speed, and suspension system …
vehicles (AVs). Many factors, such as road profile, driving speed, and suspension system …
Collision avoidance for autonomous ship using deep reinforcement learning and prior-knowledge-based approximate representation
Reinforcement learning (RL) has shown superior performance in solving sequential
decision problems. In recent years, RL is gradually being used to solve unmanned driving …
decision problems. In recent years, RL is gradually being used to solve unmanned driving …
Knowledge-enhanced generative adversarial networks for schematic design of framed tube structures
In the schematic design phase of framed tube structures, component sizing is a vital task that
requires expert experience and domain knowledge. Deep learning-based structural design …
requires expert experience and domain knowledge. Deep learning-based structural design …
Occupant-centered real-time control of indoor temperature using deep learning algorithms
This study aims to propose Real-COMFORT-a novel occupant-centered real-time indoor
temperature control system using deep learning algorithms to simultaneously optimize the …
temperature control system using deep learning algorithms to simultaneously optimize the …
DRLinFluids: An open-source Python platform of coupling deep reinforcement learning and OpenFOAM
We propose an open-source Python platform for applications of deep reinforcement learning
(DRL) in fluid mechanics. DRL has been widely used in optimizing decision making in …
(DRL) in fluid mechanics. DRL has been widely used in optimizing decision making in …
[HTML][HTML] Explainable Machine Learning (XML) to predict external wind pressure of a low-rise building in urban-like settings
This study used explainable machine learning (XML), a new branch of Machine Learning
(ML), to elucidate how ML models make predictions. Three tree-based regression models …
(ML), to elucidate how ML models make predictions. Three tree-based regression models …
A night pavement crack detection method based on image‐to‐image translation
C Liu, B Xu - Computer‐Aided Civil and Infrastructure …, 2022 - Wiley Online Library
Deep learning provides an efficient automated method for pavement condition surveys, but
the datasets used for this model are usually images taken in good lighting conditions. If …
the datasets used for this model are usually images taken in good lighting conditions. If …
Reinforcement learning-based optimal scheduling model of battery energy storage system at the building level
Installing the battery energy storage system (BESS) and optimizing its schedule to effectively
address the intermittency and volatility of photovoltaic (PV) systems has emerged as a …
address the intermittency and volatility of photovoltaic (PV) systems has emerged as a …