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Yacine Addad

Publications and source records attributed to Yacine Addad.

2 recordsLinked to original sources

Stable, Compact, and Direct Ghost-Cell Reconstruction: A Non-Iterative Approach for Embedded-Boundary Methods

A direct, analytical, non-iterative ghost-cell reconstruction framework is developed for Cartesian-grid embedded-boundary methods. Analytical expressions impose Dirichlet and Neumann boundary conditions directly at the embedded boundary, eliminating intermediate image-point reconstruction, matrix inversion, and precomputation or storage of geometry-dependent reconstruction weights. For the Cartesian stencil considered, dependencies among neighboring ghost cells form a directed acyclic graph. A topological ordering partitions ghost cells into dependency levels, permitting level-by-level reconstruction without iterative updates. The formulation is combined with hybrid ghost cells (HGC), whose centers may lie on either side of the embedded boundary. This placement satisfies the linear reconstruction-stability criterion previously derived for scalar advection while retaining a compact nearest-neighbour Cartesian stencil. An extended-stencil classical ghost-cell formulation (CGC_ES) serves as a stability-preserving reference, distinguishing the effects of reconstruction stability and stencil compactness. The same analytical relations provide solution values and spatial gradients directly on the embedded boundary, enabling evaluation of pressure forces, wall stresses, drag, and lift without separate surface reconstruction or filtering. Simulations of flow past a circular cylinder and an airfoil show that the linear criterion remains a useful indicator of reconstruction stability for the nonlinear incompressible Navier-Stokes cases considered. Classical ghost-cell reconstruction develops spurious oscillations when the criterion is violated, whereas CGC_ES and HGC remain stable. HGC additionally satisfies the stability requirement with a compact nearest-neighbour stencil.

physics.flu-dyn

Quantum-inspired space-time PDE solver and dynamic mode decomposition

The curse of dimensionality is ubiquitous in both numerical and data-driven methods. This is particularly severe for space-time methods, which treat the combined space-time domain simultaneously. We investigate the effectiveness of a quantum-inspired approach in alleviating this curse, both for solving PDEs and making data-driven predictions. We achieve this goal by treating both spatial and temporal dimensions within a single matrix product state (MPS) encoding. First, we benchmark our MPS space-time solver for both linear and nonlinear PDEs, observing that the MPS ansatz accurately captures the underlying spatio-temporal correlations while having significantly fewer degrees of freedom. Second, we develop an MPS-DMD algorithm for accurate long-term predictions of nonlinear systems, with runtime scaling logarithmically with both spatial and temporal resolution. We also demonstrate an application where both methods can be combined for cheap and accurate prediction of long-term dynamics. This research highlights the role of tensor networks in developing effective, interpretable models that bridge the gap between numerical methods and data-driven approaches.

physics.comp-ph