Gradient boosted decision trees for combustion chemistry integration

Yao, Songbai; Kronenburg, A.; Shamooni, Ali; Stein, Oliver T.; Zhang, W.

doi:10.1016/j.jaecs.2022.100077

Cited by 12 publications

(2 citation statements)

References 23 publications

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“…As could be deducted, this technique is a higher bias for lower variance. The main advantage of this method is the speeded-up training process 40 .…”

Section: Methodsmentioning

confidence: 99%

Machine learning-assisted analysis for agronomic dataset of 49 Balangu (Lallemantia iberica L.) ecotypes from different regions of Iran

Shafagh-Kolvanagh

Dehghanian

Mohammadi-Nassab

et al. 2022

Sci Rep

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The Balangu (Lallemantia iberica) species have a high gastronomical impact in the Middle East and Balkan region. It is widely used in the local food industry, such as confectionery, edible oil, and protein food. In this study, 49 ecotypes were collected from different regions of Iran. 37 agronomic traits were measured during the growing season and at harvest time. To find the correlation between the grain yield per unit area, grain yield per single plant (GYSP), oil percent (OP), and protein percent (PP) with other measured traits, which these were utilized as the labels of different machine learning (ML) procedures including Linear Regression (LR), Support Vector Regression (SVR), Random Forest Regression (RFR), and Gradient Boosting Decision Tree Regression (GBDTR). It was observed that there is a linear relationship between the measured agronomic traits and the considered labels. So, the LR, RFR, and GBDTR models showed the lowest mean absolute error, mean square error, and root mean square error than SVR models and good prediction ability of the test data. Although, the RFR and GBDTR have naturally lower bias than other methods in this study, but the GBDTR scheme is preferred because of the over-fitting shortcoming of the RFR technique. The GBDTR method showed better results rather than the other ML regression methods according to the RMSE 3.302, 0.040, 0.028, and 0.060 for GYUA, GYSP, OP, and PP, respectively.

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“…As could be deducted, this technique is a higher bias for lower variance. The main advantage of this method is the speeded-up training process 40 .…”

Section: Methodsmentioning

confidence: 99%

Machine learning-assisted analysis for agronomic dataset of 49 Balangu (Lallemantia iberica L.) ecotypes from different regions of Iran

Shafagh-Kolvanagh

Dehghanian

Mohammadi-Nassab

et al. 2022

Sci Rep

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show abstract

“…The DNN was used in simulating a turbulent non-premixed syngas oxy-flame and obtained a considerable speed-up. Yao et al [7] adopted gradient boosted decision tree (GBDT) as a machine learning approach to directly solve the chemistry ODEs and gained a speed-up of one order of magnitude.…”

Section: Introductionmentioning

confidence: 99%

DeepFlame: A deep learning empowered open-source platform for reacting flow simulations

Mao¹,

Lin²,

Zhang³

et al. 2022

Preprint

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Recent developments in deep learning have brought many inspirations for the scientific computing community and it is perceived as a promising method in accelerating the computationally demanding reacting flow simulations. In this work, we introduce DeepFlame, an open-source C++ platform with the capabilities of utilising machine learning algorithms and pre-trained models to solve for reactive flows. We combine the individual strengths of the computational fluid dynamics library OpenFOAM, machine learning framework Torch, and chemical kinetics program Cantera. The complexity of cross-library function and data interfacing (the core of DeepFlame) is minimised to achieve a simple and clear workflow for code maintenance, extension and upgrading. As a demonstration, we apply our recent work on deep learning for predicting chemical kinetics (Zhang et al. Combust. Flame vol. 245 pp. 112319, 2022) to highlight the potential of machine learning in accelerating reacting flow simulation. A thorough code validation is conducted via a broad range of canonical cases to assess its accuracy and efficiency. The results demonstrate that the convection-diffusion-reaction algorithms implemented in DeepFlame are robust and accurate for both steady-state and transient processes. In addition, a number of methods aiming to further improve the computational efficiency, e.g. dynamic load balancing and adaptive mesh refinement, are explored. Their performances are also evaluated and reported. With the deep learning method implemented in this work, a speed-up of two orders of magnitude is achieved in a simple hydrogen ignition case when performed on a medium-end graphics processing unit (GPU). Further gain in computational efficiency is expected for hydrocarbon and other complex fuels. A similar level of acceleration is obtained on an AI-specific chip -deep computing unit (DCU), highlighting the potential of DeepFlame in leveraging the next-generation computing architecture and hardware.

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Vibration-current data fusion and gradient boosting classifier for enhanced stator fault diagnosis in three-phase permanent magnet synchronous motors

Al-Haddad,

Jaber,

Hamzah

et al. 2023

Electr Eng

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Gradient boosted decision trees for combustion chemistry integration

Cited by 12 publications

References 23 publications

Machine learning-assisted analysis for agronomic dataset of 49 Balangu (Lallemantia iberica L.) ecotypes from different regions of Iran

Machine learning-assisted analysis for agronomic dataset of 49 Balangu (Lallemantia iberica L.) ecotypes from different regions of Iran

DeepFlame: A deep learning empowered open-source platform for reacting flow simulations

Vibration-current data fusion and gradient boosting classifier for enhanced stator fault diagnosis in three-phase permanent magnet synchronous motors

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