论文标题
用于身体现实的混合现实的热力学知识神经网络
Thermodynamics-informed neural networks for physically realistic mixed reality
论文作者
论文摘要
沉浸式技术在社会中的迫在眉睫的影响敦促在实时和互动物理模拟中为虚拟世界进行积极研究。在这种情况下，现实的手段符合物理定律。在本文中，我们提出了一种计算通过深度学习中混合现实中实时用户相互作用引起的（可能是非线性和耗散）可变形对象的动态响应的方法。该方法的基于图的架构确保了预测的热力学一致性，而可视化管道则可以自然而现实的用户体验。在混合现实场景中提供了与虚拟或物理固体相互作用的虚拟固体的两个示例，以证明该方法的性能。
The imminent impact of immersive technologies in society urges for active research in real-time and interactive physics simulation for virtual worlds to be realistic. In this context, realistic means to be compliant to the laws of physics. In this paper we present a method for computing the dynamic response of (possibly non-linear and dissipative) deformable objects induced by real-time user interactions in mixed reality using deep learning. The graph-based architecture of the method ensures the thermodynamic consistency of the predictions, whereas the visualization pipeline allows a natural and realistic user experience. Two examples of virtual solids interacting with virtual or physical solids in mixed reality scenarios are provided to prove the performance of the method.
