Analytically Integratable Zero-restlength Springs for Capturing Dynamic Modes Unrepresented by Quasistatic Neural Networks
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Technical Paper
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Research & Education
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DescriptionWe present a novel paradigm for modeling certain types of dynamic simulation in real-time with the aid of neural networks. Our approach utilizes a data-driven neural network only to capture quasistatic information and an analytically integratable, real-time dynamic simulation layer to augment the quasistatic neural network inference.