UQ-SHRED: uncertainty quantification of shallow recurrent decoder networks for sparse sensing via engression
arXiv:2604.01305v1 Announce Type: new Abstract: Reconstructing high-dimensional spatiotemporal fields from sparse sensor measurements is critical in a wide range of scientific applications. The SHallow REcurrent Decoder (SHRED) architecture is a recent state-of-the-art architecture that reconstructs high-quality spatial domain from hyper-sparse sensor measurement streams. An important limitation of SHRED is that in complex, data-scarce, high-frequency, or stochastic systems, portions of the spatiotemporal field must be modeled with valid uncertainty estimation. We introduce UQ-SHRED, a distributional learning framework for sparse sensing problems that provides uncertainty quantification through a neural network-based distributional regression called engression. UQ-SHRED models the uncertainty by learning the predictive distribution of the spatial state conditioned on the sensor history. By injecting stochastic noise into sensor inputs and training with an energy score loss, UQ-SHRED p
arXiv:2604.01305v1 Announce Type: new Abstract: Reconstructing high-dimensional spatiotemporal fields from sparse sensor measurements is critical in a wide range of scientific applications. The SHallow REcurrent Decoder (SHRED) architecture is a recent state-of-the-art architecture that reconstructs high-quality spatial domain from hyper-sparse sensor measurement streams. An important limitation of SHRED is that in complex, data-scarce, high-frequency, or stochastic systems, portions of the spatiotemporal field must be modeled with valid uncertainty estimation. We introduce UQ-SHRED, a distributional learning framework for sparse sensing problems that provides uncertainty quantification through a neural network-based distributional regression called engression. UQ-SHRED models the uncertainty by learning the predictive distribution of the spatial state conditioned on the sensor history. By injecting stochastic noise into sensor inputs and training with an energy score loss, UQ-SHRED produces predictive distributions with minimal computational overhead, requiring only noise injection at the input and resampling through a single architecture without retraining or additional network structures. On complicated synthetic and real-life datasets including turbulent flow, atmospheric dynamics, neuroscience and astrophysics, UQ-SHRED provides a distributional approximation with well-calibrated confidence intervals. We further conduct ablation studies to understand how each model setting affects the quality of the UQ-SHRED performance, and its validity on uncertainty quantification over a set of different experimental setups.
Executive Summary
The article introduces UQ-SHRED, a neural network-based distributional regression framework for sparse sensing problems that provides uncertainty quantification through engression, a neural network-based distributional regression technique. UQ-SHRED models the uncertainty by learning the predictive distribution of the spatial state conditioned on the sensor history, injecting stochastic noise into sensor inputs and training with an energy score loss. The framework is demonstrated on complicated synthetic and real-life datasets, providing distributional approximations with well-calibrated confidence intervals. Ablation studies are conducted to evaluate the impact of model settings on UQ-SHRED performance. The framework's effectiveness in uncertainty quantification is validated across various experimental setups.
Key Points
- ▸ UQ-SHRED provides uncertainty quantification for sparse sensing problems through engression, a neural network-based distributional regression technique.
- ▸ The framework models uncertainty by learning the predictive distribution of the spatial state conditioned on the sensor history.
- ▸ UQ-SHRED injects stochastic noise into sensor inputs and trains with an energy score loss to produce predictive distributions with minimal computational overhead.
Merits
Strength in Uncertainty Quantification
UQ-SHRED provides well-calibrated confidence intervals, enabling accurate uncertainty estimation in sparse sensing problems.
Flexibility and Adaptability
The framework's ability to adapt to various experimental setups and datasets, including turbulent flow, atmospheric dynamics, neuroscience, and astrophysics, demonstrates its flexibility and potential for real-world applications.
Demerits
Model Complexity and Interpretability
The incorporation of engression and energy score loss may increase model complexity, potentially compromising interpretability and explainability of the uncertainty quantification results.
Limited Generalizability to Non-Sparse Sensing Problems
The framework's design and evaluation are tailored to sparse sensing problems, and its generalizability to non-sparse sensing applications is unclear.
Expert Commentary
The article presents a novel and promising approach to uncertainty quantification in sparse sensing problems. UQ-SHRED's ability to learn predictive distributions and provide well-calibrated confidence intervals is a significant contribution to the field. However, the model's complexity and potential interpretability issues warrant further investigation. Additionally, the framework's generalizability to non-sparse sensing problems is unclear and requires further exploration. Nevertheless, UQ-SHRED has the potential to improve the accuracy and reliability of predictions in various real-world applications, making it a valuable tool for researchers and practitioners alike.
Recommendations
- ✓ Future research should focus on addressing the model's complexity and interpretability issues, potentially through the development of more transparent and explainable uncertainty estimation techniques.
- ✓ The framework's generalizability to non-sparse sensing problems should be explored through extensive experimentation and evaluation across various datasets and applications.
Sources
Original: arXiv - cs.LG
