Publications

A Robust Data-Free Physics-Informed Neural Network for Compressible Flows with ShocksPublished

Multiscale Physics-Informed Neural Network for Complex Fluid Flows with Long-Range DependenciesUnder Review

Hybrid Attention-Based Physics-Informed Neural Networks for Multiscale Feature ModelingIn Preparation

Turbo-PINN: A Mesh-Free Machine Learning Framework for Low-Reynolds-Number Turbine Blade Flow PredictionAccepted

Hemodynamic Modeling of Aortic and Intracranial Aneurysms Using Data-Free Physics-Informed Neural NetworksAccepted

Flow Predictions behind NACA 2412 Aerofoil Using Physics-Informed Machine Learning

Evaluation of Physics-Informed Machine Learning Approach for Computation of Fluid Flows

Effect of Corner Curvature of Square Cylinder on Flow Transition and Heat Transfer

Hybrid Attention-Based Physics-Informed Neural Networks for Multiscale Feature Modeling

On the ability of PINNs to Predict Shock-Boundary Layer Interactions (SBLIs) Without Data

Improving Turbulent Flow Datasets Using Residual-Based Attention PINN (RBA-PINN)

Trading Data for Physics: The Promise of Data-Free ML in High-Speed Flow Modelling

Prediction of Shocks Using a Consistent PINN-Based Approach