Add warning for non-positive semidefinite sigma tensor for Lorentzian susceptibility#3230
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oskooi wants to merge 2 commits into
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Add warning for non-positive semidefinite sigma tensor for Lorentzian susceptibility#3230oskooi wants to merge 2 commits into
sigma tensor for Lorentzian susceptibility#3230oskooi wants to merge 2 commits into
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stevengj
reviewed
Jun 18, 2026
| if (!sbb || !sab) continue; | ||
| LOOP_OVER_VOL(gv, ca, idx) { | ||
| const realnum a = saa[idx], b = sbb[idx], o = sab[idx]; | ||
| if (a > 0 && b > 0 && o * o > a * b * (1 + 1e-6)) return true; |
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Why is it checking "2x2" tensors, and not the full 3x3 matrix?
stevengj
reviewed
Jun 18, 2026
| // boundaries (see PR #666). sigma is static in time, so the | ||
| // predicate is the same every step; apply it branchlessly so the | ||
| // loop still vectorizes. Where s[i] == 0, p[i] and pp[i] are left | ||
| // unchanged, exactly as the original guarded update did. |
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It would be nice to know how much of a performance difference this makes in two cases:
- the chunk is completely filled with σ ≠ 0 (so
mask == 1everywhere). - the chunk is mostly σ = 0 (so that you end up doing a bunch of extra computation that would have otherwise been removed by the
if).
Collaborator
|
My other concern about this PR is that it is looking at σ on the Yee grid, for which different components of σ are stored at different Yee grid points. If σ is not constant in space, then it is problematic to simply treat the different components as part of the same σ tensor … you would need to define some effective σ tensor by some kind of spatial average of σ components. One alternative is simply to check positive-definiteness at a higher level, e.g. in the list of materials/objects in |
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Related to #3229.
This PR adds a warning if the user-supplied anisotropic Lorentzian susceptibility tensor (
sigma) is non positive semidefinite. Additionally, the guard introduced in #666 to skip update of the polarization fields at material interfaces (to ensure stability) is reorganized to enable loop vectorization.