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DeePMO: An iterative deep learning framework for high-dimensional kinetic parameter optimization
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DOI：10.1016/j.jaecs.2025.100402.png)
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Abstract
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• Boosting optimization performance by iterative sampling-learning-inference strategy. • A novel hybrid deep neural network handles both sequential and non-sequential data. • Extensive validation and ablation studies confirm method’s versatility and robustness.
Keywords:
Deep neural network
Chemical kinetics
Parameter optimization
Machine learning
Iterative strategy
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