Reconstructing material properties by deconvolution of full-field measurement images: The conductivity case
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract This study concerns the reconstruction of material parameters from full-field measurements. In this context the typical available data is a set of digital images that is seldom handled as such when solving the inverse problem. Therefore, this work investigates a direct method to compute constitutive parameter maps from full-field measurement images. Within the prototypical framework of the periodic conductivity model, the starting point for the proposed approach is the Lippmann–Schwinger equation, which is satisfied by the fields measured internally. This integral equation is reinterpreted as a linear convolution model for the sought conductivity field. Considering that multiple experiments might be available and then combined, this problem is solved in the least-square sense. To do so, the Krylov subspace-based LSQR algorithm is employed. Full advantage is taken of the convenient expression of the featured Green’s function in Fourier space and of the intensive use of the fast Fourier transform (FFT). Moreover, a spectral-based filtering regularization scheme is implemented to tackle noisy data. Overall, the proposed reconstruction algorithm only handles image-like quantities in an efficient mesh-free approach. The performance of the method is assessed on a set of synthetic 2D numerical examples both for isotropic and anisotropic material configurations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it