midas-propagate
Calibration-aware uncertainty propagation and joint re-refinement for end-to-end HEDM grain analysis.
Status: scaffold. See dev/paper/SKETCH.md
for the paper roadmap. No working code yet.
Notebooks
Worked-example Jupyter notebooks live in notebooks/. They are not shipped with pip install — get them by cloning the MIDAS repository.
What this package will do
-
Compose the existing differentiable losses across the four pipeline stages — calibration (
midas-calibrate-v2), indexing (midas-index), per-grain refinement (midas-fit-grain), and elastic inversion (midas-stress) — into a single joint NLL. -
Compute a joint MAP estimate over any subset of:
- detector calibration (Lsd, tilts, beam center, distortion)
- per-grain orientation, lattice strain, position
- global nuisance parameters (wavelength, beam profile)
- optionally per-grain elastic stress
-
Assemble the block-structured Hessian at MAP and return calibration-aware per-grain covariance via Schur-complement marginalization — no full-matrix inverse needed.
-
Propagate per-grain covariance through the elastic inversion to per-grain stress error bars via the delta method.
Why
Production HEDM tools (HEXRD, MIDAS, ImageD11, FABLE) report per-grain σ at the converged grain state with detector calibration held fixed. Downstream Bayesian crystal-plasticity work (Greeley 2026, Iyer 2025) explicitly assumes an HEDM σ no current tool can derive. This package closes that loop.
Companion packages
midas-diffract— differentiable HEDM forward modelmidas-uq— single-grain holdout / jackknife / Laplace UQmidas-joint-ff-calibrate— multi-grain + multi-detector joint refinementmidas-calibrate-v2— instrument calibrationmidas-stress— single-crystal elastic inversion
1 maintainer
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