cfd solver

2024

1. 

   Quantum-inspired framework for computational fluid dynamics

   Raghavendra Dheeraj Peddinti, Stefano Pisoni, Alessandro Marini, and 4 more authors

   Communications Physics, 2024

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   Computational fluid dynamics is both a thriving research field and a key tool for advanced industry applications. However, the simulation of turbulent flows in complex geometries is a compute-power intensive task due to the vast vector dimensions required by discretized meshes. We present a complete and self-consistent full-stack method to solve incompressible fluids with memory and run time scaling logarithmically in the mesh size. Our framework is based on matrix-product states, a compressed representation of quantum states. It is complete in that it solves for flows around immersed objects of arbitrary geometries, with non-trivial boundary conditions, and self-consistent in that it can retrieve the solution directly from the compressed encoding, i.e. without passing through the expensive dense-vector representation. This framework lays the foundation for a generation of more efficient solvers of real-life fluid problems.

   @article{peddinti2024quantum,
     title = {Quantum-inspired framework for computational fluid dynamics},
     author = {Peddinti, Raghavendra Dheeraj and Pisoni, Stefano and Marini, Alessandro and Lott, Philippe and Argentieri, Henrique and Tiunov, Egor and Aolita, Leandro},
     journal = {Communications Physics},
     volume = {7},
     number = {1},
     pages = {135},
     year = {2024},
     publisher = {Nature Publishing Group UK London},
     url = {http://dx.doi.org/10.1038/s42005-024-01623-8},
     doi = {10.1038/s42005-024-01623-8},
   }