Applications of physics-informed neural networks in power systems-a review
The advances of deep learning (DL) techniques bring new opportunities to numerous
intractable tasks in power systems (PSs). Nevertheless, the extension of the application of …
intractable tasks in power systems (PSs). Nevertheless, the extension of the application of …
[HTML][HTML] A review of physics-based machine learning in civil engineering
The recent development of machine learning (ML) and Deep Learning (DL) increases the
opportunities in all the sectors. ML is a significant tool that can be applied across many …
opportunities in all the sectors. ML is a significant tool that can be applied across many …
[PDF][PDF] Integrating physics-based modeling with machine learning: A survey
There is a growing consensus that solutions to complex science and engineering problems
require novel methodologies that are able to integrate traditional physics-based modeling …
require novel methodologies that are able to integrate traditional physics-based modeling …
Integrating scientific knowledge with machine learning for engineering and environmental systems
There is a growing consensus that solutions to complex science and engineering problems
require novel methodologies that are able to integrate traditional physics-based modeling …
require novel methodologies that are able to integrate traditional physics-based modeling …
[HTML][HTML] Machine learning for spatial analyses in urban areas: a scoping review
The challenges for sustainable cities to protect the environment, ensure economic growth,
and maintain social justice have been widely recognized. Along with the digitization …
and maintain social justice have been widely recognized. Along with the digitization …
Data learning: Integrating data assimilation and machine learning
Data Assimilation (DA) is the approximation of the true state of some physical system by
combining observations with a dynamic model. DA incorporates observational data into a …
combining observations with a dynamic model. DA incorporates observational data into a …
When physics meets machine learning: A survey of physics-informed machine learning
Physics-informed machine learning (PIML), referring to the combination of prior knowledge
of physics, which is the high level abstraction of natural phenomenons and human …
of physics, which is the high level abstraction of natural phenomenons and human …
Integrated assessment of urban overheating impacts on human life
Urban overheating, driven by global climate change and urban development, is a major
contemporary challenge that substantially impacts urban livability and sustainability …
contemporary challenge that substantially impacts urban livability and sustainability …
PIGNN-CFD: A physics-informed graph neural network for rapid predicting urban wind field defined on unstructured mesh
Urban wind field plays an important role in quantitative assessment of urban environment.
Compared to field measurement and wind tunnel experiment, Computational Fluid …
Compared to field measurement and wind tunnel experiment, Computational Fluid …
Isothermal and non-isothermal flow in street canyons: A review from theoretical, experimental and numerical perspectives
Urban street canyon flows play a central role in microclimate control, from street canyon to
neighbourhood and city scale, which affect pollutant dispersion, thermal comfort of residents …
neighbourhood and city scale, which affect pollutant dispersion, thermal comfort of residents …