Neural Modes: Self-supervised Learning of Nonlinear Modal Subspaces

Jiahong Wang; Yinwei Du; Stelian Coros; Bernhard Thomaszewski

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arXiv · 2404.17620

Neural Modes: Self-supervised Learning of Nonlinear Modal Subspaces

Jiahong Wang·Yinwei Du·Stelian Coros·Bernhard Thomaszewski

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Abstract

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.

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Jiahong Wang, Yinwei Du, Stelian Coros, Bernhard Thomaszewski. 2024-04-26. Neural Modes: Self-supervised Learning of Nonlinear Modal Subspaces. https://arxiv.org/abs/2404.17620

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