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Recurrent Neural Networks for Temporal Bioink Gelation Dynamics Prediction

Ai Biofabrication

Machine Learning for Bioink Rheology Prediction

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Ai Biofabrication › Machine Learning for Bioink Rheology Prediction

Recurrent Neural Networks for Temporal Bioink Gelation Dynamics Prediction

This study investigates recurrent neural network architectures, including LSTMs and GRUs, for modeling time-dependent rheological changes during bioink gelation and crosslinking processes. The contribution provides predictive models that capture complex temporal gel-point transitions and viscoelastic recovery, essential for optimizing printing window parameters.

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📚 Academic: Thesis & PPT assistance included🧪 Tech: Master the protocols hands-on📝 Research > 3 months: Publication co-authorship in a Scopus-indexed journal

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