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Machine learning models trained on uniform temperature data struggle with nonuniform profiles. Retraining models like Gaussian Process Regression (GPR) and VGG13 on nonuniform data overcomes these challenges for accurate temperature prediction.

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Area of Science:

  • Spectroscopy
  • Machine Learning
  • Thermodynamics

Background:

  • Laser Absorption Spectroscopy (LAS) is a powerful tool for temperature measurement.
  • Spatial temperature nonuniformity poses a significant challenge to the accuracy of LAS-based temperature prediction.
  • Existing machine learning models often fail when applied to nonuniform temperature distributions.

Purpose of the Study:

  • To investigate the impact of spatial temperature nonuniformity on machine learning algorithms for temperature prediction using LAS.
  • To evaluate the effectiveness of retraining machine learning models on nonuniform temperature data.
  • To identify machine learning models capable of overcoming the challenges posed by temperature nonuniformity.

Main Methods:

  • Trained sixteen machine learning models as surrogates for physical methods using uniform temperature distribution spectra.
  • Identified Gaussian Process Regression (GPR), VGG13, and Boosted Random Forest (BRF) as top performers on uniform data.
  • Retrained models on nonuniform temperature distribution data and evaluated their performance and generalization capabilities.
  • Utilized t-distributed Stochastic Neighbor Embedding (t-SNE) and Linear Discriminant Analysis (LDA) for data dimensionality reduction and visualization.

Main Results:

  • GPR, VGG13, and BRF performed excellently on uniform temperature profiles but degraded significantly on nonuniform profiles.
  • Retrained GPR and VGG13 models, utilizing all spectral features, demonstrated high accuracy, sensitivity, and generalization on nonuniform spectra.
  • BRF showed poor generalization on nonuniform data, indicating the impact of nonuniformity on spectral features.
  • Dimensionality reduction via t-SNE and LDA revealed distinct clustering of uniform and nonuniform temperature distribution datasets in 2D feature spaces.

Conclusions:

  • Direct application of models trained on uniform temperature data is inadequate for nonuniform conditions.
  • Retraining machine learning models, particularly those utilizing comprehensive spectral features like GPR and VGG13, can effectively overcome the negative effects of spatial temperature nonuniformity.
  • The level of nonuniformity influences regional spectral features, impacting model performance.
  • Distinct spectral characteristics differentiate uniform and nonuniform temperature distributions, enabling model differentiation.