Causal discovery in industrial systems via physics-guided variational attention and probabilistic interventions
Bibliographic record
Abstract
Industry 4.0 technologies demand robust fault detection and diagnosis systems distinguishing genuine causal relationships from spurious correlations in complex industrial processes. Traditional correlation-based approaches exhibit significant limitations with nonlinear dynamics, temporal dependencies, and uncertain operational conditions. This paper presents a physics-guided variational attention framework for causal discovery, integrating log-normal variational attention mechanisms with probabilistic interventions and domain expertise. The dual-attention architecture utilizes multivariate log-normal distributions to model asymmetric, positive-valued causal strengths, addressing limitations of symmetric Gaussian parameterizations. Physics-informed priors from operator knowledge are incorporated through Gaussian Mixture Models and transformed via moment-matching. Uncertainty quantification employs Monte Carlo sampling and conformal filtering for statistically rigorous causal validation. Evaluation across synthetic time-series data, Australian Refinery Process oscillation diagnosis, and Tennessee Eastman Process demonstrates superior performance versus baseline approaches. Log-normal variational attention consistently outperforms Gaussian alternatives, with physics-informed priors providing improvements under high-uncertainty conditions, establishing a robust foundation for industrial causal discovery applications.
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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".