Analytically Integratable Zero-restlength Springs for Capturing Dynamic Modes unrepresented by Quasistatic Neural Networks

• URL: http://arxiv.org/abs/2201.10122v1
• Date: Tue, 25 Jan 2022 06:44:15 GMT
• Title: Analytically Integratable Zero-restlength Springs for Capturing Dynamic Modes unrepresented by Quasistatic Neural Networks
• Authors: Yongxu Jin, Yushan Han, Zhenglin Geng, Joseph Teran, Ronald Fedkiw
• Abstract summary: We present a novel paradigm for modeling certain types of dynamic simulation in real-time with the aid of neural networks. We augment our quasistatic neural network (QNN) inference with a (real-time) dynamic simulation layer. We demonstrate that the spring parameters can be robustly learned from a surprisingly small amount of dynamic simulation data.
• Score: 6.601755525003559
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We present a novel paradigm for modeling certain types of dynamic simulation in real-time with the aid of neural networks. In order to significantly reduce the requirements on data (especially time-dependent data), as well as decrease generalization error, our approach utilizes a data-driven neural network only to capture quasistatic information (instead of dynamic or time-dependent information). Subsequently, we augment our quasistatic neural network (QNN) inference with a (real-time) dynamic simulation layer. Our key insight is that the dynamic modes lost when using a QNN approximation can be captured with a quite simple (and decoupled) zero-restlength spring model, which can be integrated analytically (as opposed to numerically) and thus has no time-step stability restrictions. Additionally, we demonstrate that the spring constitutive parameters can be robustly learned from a surprisingly small amount of dynamic simulation data. Although we illustrate the efficacy of our approach by considering soft-tissue dynamics on animated human bodies, the paradigm is extensible to many different simulation frameworks.

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