The 26th Computational Engineering Conference of JSCESಝพߪԍʀ‫ָ޽ࢋܯ‬୉ৈ 2020 दৈࣞThe JSCES Grand Prize 2020 Lecture and Ceremony2020 ೧ౕ‫ָ޽ࢋܯ‬୉ৈΝणৈ͠ΗͪธࠅʀΩϨϓΧϩωΠ୉ָ͹ J. S. Chen ‫گ‬द͹ಝพߪԍϑυΨΝΨϱϧ΢ϱ๎ૻ͢Ήͤɽଡ਼͹͟ࢻ௎ͳ͟ࢂՅΝ͕ଶͬ͢ͱ͕ΕΉͤɽೖ ࣎ / Date̐̎̐̏೧݆̓̐̕ೖʤ໨ʥ̏̓ʁ̎̓ʷ̏̓ʁ̓̓*ʤश྅‫ޛ‬ɼदৈࣞͶҢߨ͢Ήͤʥ‫ ࣞ ܙ‬/ Form࢚ճͶΓΖ়ղ͹‫ޛ‬ɾJ. S. Chen ઎ਫ਼͹ಝพߪԍϑυΨΝ๎ૻ͢Ήͤɽ࢚ ճ / Chair

Junji Kato (Nagoya University)

Deep Autoencoders Enhanced Manifold
Learning for Digital Twin Application toMusculoskeletal Systems
J. S. Chen #, Xiaolong He, Karan Taneja# William Prager Chair Professor, Department of Structural Engineering & Centerfor Extreme Events Research, University of California, San Diego, USA
https://jschen.eng.ucsd.edu/Abstract

The goal of this research is to develop a two-scale Digital Twin

which integrates cellular scale musculoskeletal (MSK) models with structural scale system
dynamics model for simulating human behavior under different environments, conditions, andconstraints. The cellular scale models combine the image based physical models augmented with
data-driven computing and learning algorithms for simulating muscle mechanics. Under thisframework, the data-driven modeling is a hybrid approach that integrates universal physical lawswith sensor data directly to circumvent the necessity of using phenomenological constitutivemodels. A robust data-driven simulation approach based on manifold learning based locallyconvex data-driven (LCDD) computing, is formulated under the image based RKPM
framework. The proposed approach reconstructs a local material manifold with the convex hull based on the nearest experimental data to the given
state, and seeks for the optimum solution via the projection onto the associated local manifold. An autoencoder is introduced to extract a lowdimensional representation (embedding) of data and addresses the “dimensionality curse” emerging from other conventional manifold learning-based
methods. The data embeddings learned by the encoders provide enhanced noise filtering and extrapolation generalization. A local convexitypreserving scheme based on Shepard interpolation is introduced for the data-driven local solution to enhance numerical stability.
The cellular scale models are upscaled with the reduced order approach for coupling with the component and whole-body system dynamics models.
The system dynamics models are calibrated with the motion tracking data by solving an optimization problem. This framework is formulated byenhancing Deep Neural Networks (DNNs) with physics-based constraints such as residuals of the governing equations. Adding the physics-based
residual as a regularization term to the loss function of DNNs allows for a robust system identification procedure. The approach is further enhancedwith modern machine learning architectures to yield a computationally efficient framework for system parameters and function identification. The
future work in integrating various data streams of the subject including motion capture data (position, velocity, and acceleration) and wearable sensor
data (kinetic and kinematic data of the muscle groups) for a better solution accuracy and connecting the system identification of the multi-bodydynamics of the MSK DT to the patient specific muscle mechanical properties, is also
highlighted.
* ༩ఈ࣎ࠃͳ͵ΕΉͤɽ