Machine learning (ML) provides novel and powerful ways of accurately and efficiently
recognizing complex patterns, emulating nonlinear dynamics, and predicting the spatio …

Reynolds-averaged Navier–Stokes simulations represent a cost-effective option for practical
engineering applications, but are facing ever-growing demands for more accurate …

Uncertainties in ocean-mixing parameterizations are primary sources for ocean and climate
modeling biases. Due to lack of process understanding, traditional physics-driven …

In this paper, we present two novel physics-infused neural network (NN) architectures that
satisfy various invariance conditions for constructing efficient and robust Deep Learning (DL) …

The research on tropical cyclone (TC) relieson numerical models and simulations, with the
currently widely used boundary layer parameterization posing a significant challenge on …

The uncertainty of ocean turbulent mixing parameterization comprises a significant
challenge in ocean and climate models. A depth-dependent deep learning ocean turbulent …

In this paper major machine learning (ML) tools and the most important applications
developed elsewhere for numerical weather and climate modeling systems (NWCMS) are …

From the simplest models to complex deep neural networks, modeling turbulence with
machine learning techniques still offers multiple challenges. In this context, the present …

The effect of the rotation on the turbulent mixing of two miscible fluids of small contrasting
density, induced by Faraday instability, is investigated using direct numerical simulations …

In upscaling methods, closures for nonlinear problems present a well-known challenge.
While a number of theoretical methods have been proposed for handling such closures …