A feature-encoded physics-informed parameter identification neural network for musculoskeletal systems

HIGHLIGHTS

• who: Karan Taneja et al. from the Department of Structural Engineering, University of California San Diego have published the paper: A Feature-Encoded Physics-Informed Parameter Identification Neural Network for Musculoskeletal Systems, in the Journal: (JOURNAL) of September/19,/2022
• what: The authors propose a physics-informed parameter identification neural_network (PI-PINN) for the simultaneous prediction of motion and parameter identification with application to MSK systems. Given the plex`s in the model is represented as FMT (a; q; q; muscle activation signals from Eq and parameters of involved muscle groups, the generalized angular motions . . .
  

   

  

  If you want to have access to all the content you need to log in!

  Thanks :)

  Username or E-mail:

  If you don't have an account, you can create one here.