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Can I read A Physics-informed Neural Network-based Surrogate Framework to Predict Moisture Concentration and Shrinkage of a Plant Cell During Drying on EtoBox?
A Physics-informed Neural Network-based Surrogate Framework to Predict Moisture Concentration and Shrinkage of a Plant Cell During Drying by C.P. Batuwatta-Gamage; C.M. Rathnayaka; H.C.P. Karunasena; W.D.C.C. Wijerathne; H. Jeong; Z.G. Welsh; M.A. Karim; Y.T. Gu is a Agricultural and Biological Sciences article available to read on EtoBox.
What is A Physics-informed Neural Network-based Surrogate Framework to Predict Moisture Concentration and Shrinkage of a Plant Cell During Drying about?
This paper presents a Physics-Informed Neural Network-based (PINN-based) surrogate framework, which can couple time-based moisture concentration and moisture-content-based shrinkage of a plant cell during drying. For this, a set of differential equations are coupled to two distinct multilayer feedforward neural networks: (a) PINN-MC to predict Moisture Concentration (MC) with Fick's law of diffusion; and (b) PINN-S to predict Shrinkage (S) with ‘free shrinkage’ hypothesis. Results indicate that compared to a regular deep neural network (DNN), the PINN-MC with fundamental physics guidance produces 53% and 81% accuracy values when unknown data has the lowest five timesteps and the lowest 27 data points, respectively. Moreover, its accuracy is 80% better when predicting any unknown spatiotemporal domain variations. PINN-MC further demonstrates stable and accurate MC predictions irrespective of drying process parameters and microstructural variations. In addition, the PINN-S separately proves that utilising a derived relationship based on the ‘free shrinkage’ hypothesis can improve shrinkage predictions into a realistic behaviour. Also, the PINN-based surrogate framework combines multi
Who reads A Physics-informed Neural Network-based Surrogate Framework to Predict Moisture Concentration and Shrinkage of a Plant Cell During Drying?
It is typically read by researchers, students, and practitioners in Agricultural and Biological Sciences.
- Author
- C.P. Batuwatta-Gamage; C.M. Rathnayaka; H.C.P. Karunasena; W.D.C.C. Wijerathne; H. Jeong; Z.G. Welsh; M.A. Karim; Y.T. Gu
- Publisher
- Elsevier BV
- Published
- 2022
- Language
- EN
- Field
- Agricultural and Biological Sciences (Life Sciences)