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Grid: Computational Foundation - k-Wave-python documentation

k-wave-python.readthedocs.io · 140 words · saved by 1 readers

The computational grid defines the spatial and temporal discretization for k-Wave simulations. It forms one of the four core components (Grid, Medium, Source, Sensor) that define every acoustic simulation. Grid Spacing: Determines simulation accuracy. Use Δ 𝑥 ≤ 𝜆 min / 3 where 𝜆 min is the smallest wavelength. k‑Wave: Nyquist limit = 2 PPW; k‑Wave defaults to 3 PPW as a safer minimum — increase to ~6–15+ PPW for nonlinear/heterogeneous problems. Grid Size: Total number of points. Larger grids provide finer resolution but increase computational cost significantly. Time Step: Computed from the CFL condition (e.g., Δ 𝑡 = CFL ⋅ min ( Δ 𝑥 ) / 𝑐 max ). You can override CFL for custom temporal sampling. See options. For detailed tutorials and parameter-selection guidelines, see ../fundamentals/understanding_grids.

The computational grid defines the spatial and temporal discretization for k-Wave simulations. It forms one of the four core components (Grid, Medium, Source, Sensor) that define every acoustic simulation. Key Concepts¶ Grid Spacing: Determines simulation accuracy. Use \(\Delta x \le \lambda_\mathrm{min}/3\) where \(\lambda_\mathrm{min}\) is the smallest wavelength. k‑Wave: Nyquist limit = 2 PPW; k‑Wave defaults to 3 PPW as a safer minimum — increase to ~6–15+ PPW for nonlinear/heterogeneous problems. Grid Size: Total number of points. Larger grids provide finer resolution but increase…

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