GELNO-FD: gauge-equivariant Fourier liquid neural operators for interpretable Markovian Bayesian dynamics
Bibliographic record
Abstract
We describe GELNO-FD, an innovative framework that combines Gauge-Equivariant Liquid Neural Operators with Fourier-domain representations to model intricate spatiotemporal dynamics amidst uncertainty. By in- corporating gauge-equivariant structures into the neural operator framework, GELNO-FD guarantees uniform physical symmetry across dynamic fields, while the Fourier-based architecture facilitates fast global context modeling. Additionally, we integrate Markovian temporal dependencies with a Bayesian inference layer to improve interpretability and uncertainty quantification, allowing the model to learn structured stochastic transitions and disseminate calibrated confidence estimates. Experimental findings from both synthetic and real-world dynamical systems illustrate GELNO-FD’s exceptional efficacy in predicting, resilience to distribution shifts, and dependability in decision-critical contexts. This study integrates equivariant learning, operator-based modeling, and probabilistic reasoning to enhance reliable AI for physics-informed and real-world dynamics.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".