Optimization of Physics-Informed Neural Networks for Efficient Surrogate Modeling of Huxley's Muscle Model in Multi-Scale Finite Element Simulations-Reference-Cited by-同舟云学术

Optimization of Physics-Informed Neural Networks for Efficient Surrogate Modeling of Huxley's Muscle Model in Multi-Scale Finite Element Simulations

Published:2023-12-04 Issue: Volume: Page:457-461
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Container-title:2023 IEEE 23rd International Conference on Bioinformatics and Bioengineering (BIBE)
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Author:

Milićević Bogdan¹,Ivanović Miloš²,Stojanović Boban²,Milošević Miljan³,Milovanović Vladimir⁴,Kojić Miloš⁵,Filipović Nenad¹

Affiliation:

1. University of Kragujevac,Faculty of Engineering, Bioengineering Research and Development Center (BioIRC),Kragujevac,Serbia

2. University of Kragujevac,Faculty of Science, Bioengineering Research and Development Center (BioIRC),Kragujevac,Serbia

3. Institute for Information Technologies, University of Kragujevac, Belgrade Metropolitan University,Bioengineering Research and Development Center (BioIRC) Kragujevac,Belgrade,Serbia

4. University of Kragujevac,Faculty of Engineering,Kragujevac,Serbia

5. Serbian Academy of Sciences and Arts, Belgrade, Houston Methodist Research Institute,Bioengineering Research and Development Center (BioIRC), Kragujevac,Houston,USA

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10431814/10431802/10431869.pdf?arnumber=10431869

Reference25 articles.

1. Multi-scale striated muscle contraction model linking sarcomere length-dependent cross-bridge kinetics to macroscopic deformation

2. Coupling finite element and huxley models in multiscale muscle modeling

3. Distributed multi-scale muscle simulation in a hybrid MPI–CUDA computational environment

4. Machine learned domain decomposition scheme applied to parallel multi-scale muscle simulation

5. An efficient multiscale surrogate modelling framework for composite materials considering progressive damage based on artificial neural networks