Robust PINN modeling via sensitivity-based adaptive sampling: Integration of optimal sensor placement and structural uncertainty handling
Bibliographic record
Abstract
Physics-informed neural networks (PINNs) have emerged as promising surrogate models for systems governed by partial differential equations (PDEs), yet their practical implementation faces challenges in training efficiency and robustness to uncertainties. This study refines and improves a recently proposed sensitivity-based adaptive sampling (SBS) methodology to address these challenges specifically for twice-differentiable PDE systems. We first conduct a systematic investigation of SBS hyper-parameters, including prediction horizon and adaptation rate, revealing their crucial role in training performance. To enhance PINN model robustness facing uncertainties, we propose two approaches: (1) incorporating sensor measurements at sensitivity-identified locations into the loss function, and (2) augmenting the PINN architecture with direct sensor data inputs. Results show that our proposed approaches achieve superior generalization capabilities and robustness. Furthermore, we demonstrate that the SBS methodology can serve for optimal sensor placement by identifying locations that maximize information gain for model training.
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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 | 0.001 |
| 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 | 0.001 |
| 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".