We propose a self-supervised approach for learning physics-based subspaces
for real-time simulation. Existing learning-based methods construct subspaces
by approximating pre-defined simulation data in a purely geometric way.
However, this approach tends to produce high-energy configurations, leads to
entangled latent space dimensions, and generalizes poorly beyond the training
set. To overcome these limitations, we propose a self-supervised approach that
directly minimizes the system's mechanical energy during training. We show that
our method leads to learned subspaces that reflect physical equilibrium
constraints, resolve overfitting issues of previous methods, and offer
interpretable latent space parameters.

我们提出一种自监督方法，用于学习基于物理的子空间进行实时模拟。通过近似预定义的模拟数据以纯几何方式构建子空间的现有基于学习的方法。然而，这种方法倾向于产生高能量配置，导致了纠缠的潜空间维度，并且在训练集之外的泛化能力较差。为了克服这些限制，我们提出了一种自监督方法，在训练期间直接最小化系统的机械能。我们证明了我们的方法导致了学习到反映物理平衡约束的子空间，解决了先前方法的过拟合问题，并提供可解释的潜空间参数。