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Explicit Euler characteristic backtracking with multilinear interpolation
Definition
Inputs are a periodic grid from Periodic tensor grids and multilinear interpolation, a finite horizon , a positive integer of time steps, a periodic velocity , periodic initial data , node values , and a nonnegative integer step budget . Write and . The output is either success with , or budget-exhausted with no partial final state.
- If , return
budget-exhaustedbefore changing the input data. - For and each arrival node , compute the explicit backward Euler foot lift Reduce each coordinate modulo its period when evaluating the periodic interpolant.
- Set
- After the finite updates, return
successwith all node arrays.
The finite step budget counts time updates. Every update evaluates one foot per grid node and a finite sum of interpolation terms. The procedure imposes no bound on ; the floor-and-fraction convention in the interpolation definition applies even when a foot crosses many grid cells.
Remarks
This algorithm is the periodic-box branch. It does not wrap a foot through a nonperiodic boundary. An inflow/exit implementation must instead use an explicit first-hit oracle and return an unresolved status when the first hit or its error certificate is not resolved; A transverse backward boundary hit has a certified time and trace error bound records the transverse error interface. No grazing, simultaneous, or unbounded event sequence is hidden in the periodic update.
The algorithm defines tensor-product multilinear interpolation only. A higher-order replacement needs its own operator-norm or limiter bound and its own interpolation defect estimate; high order alone does not imply contractivity.
Depends on
Used by
- A constant-speed foot can cross many cells in one stable update Example
- Refining time alone leaves interpolation diffusion on a fixed grid Example
- Variable-foot pointwise interpolation can change the grid sum Example
- Explicit Euler gives a second-order local backward-foot defect Lemma
- Periodic semi-Lagrangian error is controlled by local foot and interpolation defects Theorem
Dependency tree · two levels
8 results within two dependency steps of this one, each drawn at its shortest distance from it. An arrow runs from a result to what uses it, so the chart reads left to right and ends at this result, which carries a heavier outline. Every node is a link to that result. Click elsewhere on the chart to enlarge it.
Sources
- European Centre for Medium-Range Weather Forecasts, Numerical Methods, Meteorological Training Course Lecture Series, 2002 (standard reference, not scraped)
- Lukas Einkemmer and Alexander Ostermann, On the error propagation of semi-Lagrange and Fourier methods for advection problems, Computers & Mathematics with Applications 69 (2015), 170–179 (standard reference, not scraped)