1 citations · 1 across the 2 of their papers we have counts for
2 papers
cs.CE2026
Accelerated and data-efficient flow prediction in stirred tanks via physics-informed learning
Mahdi Naderibeni, Liang Wu, David M. J. Tax
The simulation of fluid flows is computationally expensive due to the complexity of its governing partial differential equations. Machine learning models offer a potential surrogat…
cs.CE2024★ 1 cited
Learning solutions of parametric Navier-Stokes with physics-informed neural networks
M. Naderibeni, M. J. T. Reinders, L. Wu +1
We leverage Physics-Informed Neural Networks (PINNs) to learn solution functions of parametric Navier-Stokes Equations (NSE). Our proposed approach results in a feasible optimizati…