Author(s):
1. Bogdan Milićević, Fakultet inženjerskih nauka Univerziteta u Kragujevcu, Serbia
2. Miloš Ivanović, Univerzitet u Kragujevcu, Prirodno-matematički fakultet, , Serbia
3. Boban Stojanović, Univerzitet u Kragujevcu, Prirodno-matematički fakultet, , Serbia
4. Nenad Filipović, Univerzitet u Kragujevcu, Serbia

Abstract:
Biophysical muscle models, also known as Huxley-type models, are appropriate for simulating non-uniform and unsteady contractions. Large-scale simulations can be more challenging to use because this type of model can be computationally intensive. The method of characteristics is typically used to solve Huxley's muscle equation, which describes the distribution of connected myosin heads to the actin-binding sites. Once this equation is solved, we can determine the generated force and the stiffness of the muscle fibers, which may then be employed in the macro-level simulations of finite element analysis. In our paper, we developed a physics-informed surrogate model that functions similarly to the original Huxley muscle model but uses a lot less computational resources in order to enable more effective use of the Huxley muscle model.

Key words:
physics-informed neural networks, numerical analysis, machine learning, Huxley’s muscle model,physics-informed neural networks, numerical analysis, machine learning, Huxley’s muscle model

Thematic field:
SYMPOSIUM B - Biomaterials and nanomedicine

Date of abstract submission:
28.07.2022.

Conference:
Contemporary Materials 2022 - Savremeni materijali

Abstract