Abraham, Blesso
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Modeling chemical kinetics of geopolymers using physics informed neural network Abraham, Blesso; Sudhakar, Thirumalaivasal Devanathan
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 14, No 3: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v14i3.pp822-829

Abstract

Using a physics informed neural network for the analysis of geopolymers as an alternate material for cement can be a viable approach, as neural networks are capable of modeling complex, nonlinear relationships in data, which can be beneficial for representing the dynamics of chemical properties. If you have a substantial amount of theoretical data, a neural network can learn patterns and relationships in the data, even when the underlying system dynamics are not well-defined or are difficult to model analytically. A welltrained neural network can generalize from the training data to make predictions for unseen scenarios, which can be useful for real-time analysis of the material.