6 papers
A Multi-Fidelity Parametric Framework for Reduced-Order Modeling using Optimal Transport-based Interpolation: Applications to Diffused-Interface Two-Phase Flows
Moaad Khamlich, Niccolò Tonicello, Federico Pichi +1
This work introduces a data-driven, non-intrusive reduced-order modeling (ROM) framework that leverages Optimal Transport (OT) for multi-fidelity and parametric problems in two-pha…
A stochastic perturbation approach to nonlinear bifurcating problems
Isabella Carla Gonnella, Moaad Khamlich, Federico Pichi +1
Incorporating probabilistic terms in mathematical models is crucial for capturing and quantifying uncertainties in real-world systems, especially when the solution is not unique or…
Sparse Identification for bifurcating phenomena in Computational Fluid Dynamics
Lorenzo Tomada, Moaad Khamlich, Federico Pichi +1
This work investigates model reduction techniques for nonlinear parameterized and time-dependent PDEs, specifically focusing on bifurcating phenomena in Computational Fluid Dynamic…
Projection-based model order reduction for residence time distribution analysis of an industrial-scale continuous casting tundish
Harshith Gowrachari, Mattia Giuseppe Barra, Moaad Khamlich +3
The flow behavior in the continuous casting tundish plays a critical role in steel quality and is typically characterized via residence time distribution (RTD) curves. This study i…
Efficient Numerical Strategies for Entropy-Regularized Semi-Discrete Optimal Transport
Moaad Khamlich, Francesco Romor, Gianluigi Rozza
Semi-discrete optimal transport (SOT), which maps a continuous probability measure to a discrete one, is a fundamental problem with wide-ranging applications. Entropic regularizati…
Optimal Transport-inspired Deep Learning Framework for Slow-Decaying Kolmogorov n-width Problems: Exploiting Sinkhorn Loss and Wasserstein Kernel
Moaad Khamlich, Federico Pichi, Gianluigi Rozza
Reduced order models (ROMs) are widely used in scientific computing to tackle high-dimensional systems. However, traditional ROM methods may only partially capture the intrinsic ge…