Parameter estimation of enzymatic reaction systems based on physics-informed machine learning
To reveal the potential of physics-informed neural networks in biochemistry,a new parameter estimation method based on modem physics-informed machine learning tools was investigated and its function was demonstrated through a case study of enzymatic synthesis process and the effects of soft and hard boundary constraint settings were compared on the computational results.The experimental results show that both physics-informed neural networks with soft and hard constraints can accurately estimate model parameters,with goodness of fit R2 above 0.98 on all observable variables.The resulting system model can better reflect the dynamic process of the system.The proposed method combines the advantages of model-driven and data-driven approaches and achieves robust training results on a small dataset based on 40 noisy samples,significantly reducing the required data.
国家科技期刊平台
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