A Fast and Generalizable ML-Assisted Framework for Full-Wave Inverse Scattering
Bibliographic record
Abstract
This paper proposes a novel machine learning (ML)-assisted framework for solving full-wave inverse scattering problems (ISPs) in inhomogeneous, high-contrast media. Traditional deterministic algorithms used to solve such ISPs face significant challenges due to their high computational cost, inherent nonlinearity, and strong ill-posedness. Recently, the introduction of ML methods has enabled the development of rapid solutions to this problem. However, these solutions’ limited out-of-distribution (OOD) generalization capabilities pose significant challenges for practical applications. To address these challenges, we propose a novel pathway to combine ML models with full-wave inversion (FWI). In this framework, ML models serve as auxiliary tools, supplying prior knowledge for use in FWI. A mathematically guaranteed bounds-generation algorithm is proposed to bridge ML models with FWI, and a limited-memory Broyden–Fletcher–Goldfarb–Shanno algorithm with bound constraints (L-BFGS-B) is introduced in FWI to incorporate these bounds. In contrast to existing research, our framework leverages ML to enhance computational speed while preserving the interpretability and broad applicability of physical models, making it outstanding for OOD samples. We validate the framework across three numerical datasets and conducted rigorous ablation studies on each component to confirm its contributions. To further assess the robustness of the framework, we perform a noise stability study under perturbed conditions. In addition, we extend the framework to multi-frequency and time-domain inversion schemes, thereby demonstrating its broad applicability across diverse FWI tasks. We also integrate transfer learning techniques to highlight the framework’s strong compatibility with emerging ML techniques.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".