Simulation-Augmented Physics-Aware Neural Networks for Nonlinear Inverse Problems
Bibliographic record
Abstract
This study proposes a novel approach to inverse problems in the aerospace domain, with a particular focus on orbit determination. By leveraging deep learning, our framework infers the underlying physical parameters from observed data, even in the presence of highly nonlinear forward models. Crucially, we consider settings where the dimensionality of the physical parameters is substantially lower than that of the observations. Our method trains a neural network with a hybrid loss function that combines observational data and simulated data derived from an imperfect physical model. This joint utilization of actual and simulated data enables effective learning by pairing simulated observations with their corresponding parameter values. Experimental evaluations, including orbit determination tasks, highlight the advantages of our Simulation-augmented Physics-Aware Neural Networks (SimPANNs) over traditional methods that rely solely on observational data. Not only does our approach enhance both accuracy and robustness, but it also demonstrates a promising path for solving nonlinear inverse problems in reduced-parameter spaces by unifying physics-based modeling with data-driven techniques.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".