Physics-informed neural networks for optimal vaccination plan in SIR epidemic models
Bibliographic record
Abstract
This work investigates the minimum eradication time in a controlled susceptible-infectious-recovered model with constant infection and recovery rates. The eradication time is defined as the earliest time the infectious population falls below a prescribed threshold and remains below it. Leveraging the fact that this problem reduces to solving a Hamilton-Jacobi-Bellman (HJB) equation, we propose a mesh-free framework based on a physics-informed neural network to approximate the solution. Moreover, leveraging the well-known structure of the optimal control of the problem, we efficiently obtain the optimal vaccination control from the minimum eradication time using the dynamic programming principle. To improve training stability and accuracy, we incorporate a variable scaling method and provide theoretical justification through a neural tangent kernel analysis. Numerical experiments show that this technique significantly enhances convergence, reducing the mean squared residual error by approximately 80% compared with standard physics-informed approaches. Furthermore, the method accurately identifies the optimal switching time. These results demonstrate the effectiveness of the proposed deep learning framework as a computational tool for solving optimal control problems in epidemic modeling as well as the corresponding HJB equations.
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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 | 0.000 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".