Digital Twins for Solids and Structures

Data-driven computational simulation of soft biomaterials

Schematic of Data Driven Computing for Biomechanics


Relevant papers:

  • He, Q. & Chen, J. S. (2019). A Physics-Constrained Data-Driven Approach Based on Locally Convex Reconstruction for Noisy Database. Computer Methods in Applied Mechanics and Engineering, 363, 112791.
  • Zhang, Y., Chen, J. S., He, Q., He, X., Basava, R., Hodgson, J., Sinha, U. & Sinha, S. (2019). Microstructural Analysis of Skeletal Muscle Force Generation During Ageing. International Journal for Numerical Methods in Biomedical Engineering, 36(1), e3295.
  • He, Q., Laurence, D., Lee, C. H., Chen, J. S. (2020). Manifold learning-based data-driven modeling for soft biological tissues. Journal of Biomechanics, 117, 110124.
  • He, X., He, Q., Chen, J. S. (2021). Deep autoencoders for physics-constrained data-driven nonlinear materials modeling. Computer Methods in Applied Mechanics and Engineering, 385, 114034.
  • Taneja, K., He, X., He, Q., Zhao, X., Lina, Y., Loh, K., Chen, J.S. (2022) A Feature-Encoded Physics-Informed Parameter Identification Neural Network for Musculo-Skeletal Systems, Journal of Biomechanical Engineering.
  • Taneja, K., He, X., He, Q., & Chen, J. S. (2024). A Multi-Resolution Physics-Informed Recurrent Neural Network: Formulation and Application to Musculoskeletal Systems. Computational Mechanics.
  • Du, H., Guo, B., He, Q. (2024) Differentiable Neural-Integrated Meshfree Method for Forward and Inverse Modeling of Finite Strain Hyperelasticity, Engineering with Computers, 1-21.