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[2410.18424] A Causal Graph-Enhanced Gaussian Process Regression for Modeling Engine-out NOx
Summary
This paper presents a novel probabilistic model using Gaussian process regression to predict engine-out NOx emissions, enhancing predictive performance through causal graph integration.
Why It Matters
Accurate modeling of NOx emissions is crucial for meeting regulatory standards and improving environmental outcomes. This research addresses the limitations of traditional deterministic models by introducing a probabilistic framework that incorporates causal graphs, potentially leading to better diagnostics and monitoring in automotive applications.
Key Takeaways
- The study develops a probabilistic model for predicting NOx emissions using Gaussian process regression.
- Incorporating causal graphs into the model significantly enhances predictive accuracy.
- The model outperforms traditional methods, providing a more reliable framework for real-time monitoring.
Computer Science > Machine Learning arXiv:2410.18424 (cs) [Submitted on 24 Oct 2024 (v1), last revised 24 Feb 2026 (this version, v3)] Title:A Causal Graph-Enhanced Gaussian Process Regression for Modeling Engine-out NOx Authors:Shrenik Zinage, Ilias Bilionis, Peter Meckl View a PDF of the paper titled A Causal Graph-Enhanced Gaussian Process Regression for Modeling Engine-out NOx, by Shrenik Zinage and 2 other authors View PDF HTML (experimental) Abstract:The stringent regulatory requirements on nitrogen oxides (NOx) emissions from diesel compression ignition engines require accurate and reliable models for real time monitoring and diagnostics. Although traditional methods such as physical sensors and virtual engine control module (ECM) sensors provide essential data, they are only used for estimation. Ubiquitous literature primarily focuses on deterministic models with little emphasis on capturing the various uncertainties. The lack of probabilistic frameworks restricts the applicability of these models for robust diagnostics. The objective of this paper is to develop and validate a probabilistic model to predict engine-out NOx emissions using Gaussian process regression. Our approach is as follows. We employ three variants of Gaussian process models: the first with a standard radial basis function kernel with input window, the second incorporating a deep kernel using convolutional neural networks to capture temporal dependencies, and the third enriching the deep kernel ...
