Physics-informed neural networks for highly compressible flows: assessing and enhancing shock-capturing capabilities

While physics-informed neural networks have been shown to accurately solve a wide range of fluid dynamics problems, their effectivity on highly compressible flows is so far limited. In particular, they struggle with transonic and supersonic problems that involve discontinuities such as shocks. While there have been multiple efforts to alleviate...

Reduced Order Modelling and Model-Free Prediction of Chaotic Systems Using Deep Learning

Most physical systems of interest are chaotic in nature. Quick and reasonably accurate solutions for these systems are essential to various fields such as the effective control mechanism construction and early-stage design. However, their chaotic nature also leads to them being computationally expensive to model using traditional numerical...

On leveraging physical knowledge to augment neural network-based surrogate models for simulations

The computational cost of high-fidelity engineering simulations, for example CFD, is prohibitive if the application requires frequent design iterations or even fully fledged optimization. A popular way to reduce the computational cost and enable fast iteration cycles is to use surrogate models that are trained to predict simulation results from...

Unsteady SpaRTA: Data-driven turbulence modelling for unsteady applications

Recent years have seen an increase in studies focusing on data-driven techniques to enhance modelling approaches like the two-equation turbulence models of Reynolds-averaged Navier-Stokes (RANS). Different techniques have been implemented to improve the results from these simulations. In particular, the main focus has been on overcoming the...

Velocity Reconstruction in Pool Fires using Physics-Informed Machine Learning

In many flow experiments it is complex to measure all flow states of interest, leading to the need for a method to retrieve unmeasured flow states from measured ones. This work focuses on Hidden Fluid Mechanics (HFM), which refers to a Physics-Informed Neural Network (PINN) able to incorporate the Navier-Stokes (NS) equations into the loss...

Machine learning-based identification of precursors of extreme events in chaotic systems

Abrupt and rapid high-amplitude changes in a dynamical system’s states known as extreme events appear in many processes occurring in nature, such as drastic climate patterns, rogue waves, or avalanches. These events often entail catastrophic effects, therefore their description and prediction is of great importance. Nevertheless, because of...