6 papers
Model Order Reduction of Cerebrovascular Hemodynamics Using POD_Galerkin and Reservoir Computing_based Approach
Rahul Halder, Arash Hajisharifi, Kabir Bakhshaei +1
We investigate model order reduction (MOR) strategies for simulating unsteady hemodynamics within cerebrovascular systems, contrasting a physics-based intrusive approach with a dat…
Coupling Physics Informed Neural Networks with External Solvers
Rahul Halder, Giovanni Stabile, Gianluigi Rozza
The current work aims to incorporate physics-based loss in Physics Informed Neural Network (PINN) directly using the numerical residual obtained from the governing equation in any…
A Deep-Learning Enhanced Gappy Proper Orthogonal Decomposition Method for Conjugate Heat Transfer Problem
Arash Hajisharifi, Rahul Halder, Michele Girfoglio +2
The current study aims to develop a non-intrusive Reduced Order Model (ROM) to reconstruct the full temperature field for a large-scale industrial application based on both numeric…
A LSTM-enhanced surrogate model to simulate the dynamics of particle-laden fluid systems
Arash Hajisharifi, Rahul Halder, Michele Girfoglio +3
The numerical treatment of fluid-particle systems is a very challenging problem because of the complex coupling phenomena occurring between the two phases. Although accurate mathem…
Physics Informed Neural Network Framework for Unsteady Discretized Reduced Order System
Rahul Halder, Giovanni Stabile, Gianluigi Rozza
This work addresses the development of a physics-informed neural network (PINN) with a loss term derived from a discretized time-dependent reduced-order system. In this work, first…
Deep Learning-Driven Nonlinear Reduced-Order Models for Predicting Wave-Structure Interaction
Rahul Halder, Murali Damodaran, Khoo Boo Cheong
Long Short-Term Memory (LSTM) network-driven Non-Intrusive Reduced Order Model (NROM) for predicting the dynamics of a floating box on the water surface in a wavemaker basin is add…