6 papers
Conserved and relaxing modes enable polynomial-success coherent Carleman lattice Boltzmann evolution
Hua-Dong Yao, Sauro Succi
Carleman linearization represents nonlinear lattice Boltzmann collision dynamics in a form amenable to quantum block encoding, but low postselection probabilities in existing popul…
Representation-split Carleman lattice Boltzmann with near-unit step success probability on quantum computers
Hua-Dong Yao, Sauro Succi
The Carleman lattice Boltzmann method replaces the quadratic collision of the lattice Boltzmann equation by a linear map on a lifted state. Existing block encodings of this map suc…
Quadrature-Aware Complex-Linear Neural Operator for Boundary-to-Field Prediction in Resonant Acoustics
Muhammad Idrees Khan, Hua-Dong Yao
Repeated prediction of acoustic fields from spatially distributed boundary excitation is computationally expensive when each source realization requires a new wave simulation. This…
Deterministic Realization of Classical Dissipation on Quantum Computers
Muhammad Idrees Khan, Sauro Succi, Hua-Dong Yao
Lattice Boltzmann (LB) on quantum devices must reconcile unitary gate evolution with the dissipative \emph{collision} step. In the multiple-relaxation-time (MRT) class, we work in…
Physics-Constrained Neural Closure for Lattice Boltzmann Large-Eddy Simulation
Muhammad Idrees Khan, Sauro Succi, Hua-Dong Yao +1
We present a physics-constrained, data-driven subgrid-scale (SGS) stress closure for large-eddy simulation (LES) in the lattice Boltzmann method (LBM). Trained on filtered-downsamp…
Physics informed data-driven near-wall modelling for lattice Boltzmann simulation of high Reynolds number turbulent flows
Xiao Xue, Shuo Wang, Hua-Dong Yao +2
Data-driven approaches offer novel opportunities for improving the performance of turbulent flow simulations, which are critical to wide-ranging applications from wind farms and ae…