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。

神经模式：非线性模态子空间的自监督学习

Neural Modes: Self-supervised Learning of Nonlinear Modal Subspaces

Fish in schooling formations navigate complex flow-fields replete with
mechanical energy in the vortex wakes of their companions. Their schooling
behaviour has been associated with evolutionary advantages including collective
energy savings. How fish harvest energy from their complex fluid environment
and the underlying physical mechanisms governing energy-extraction during
collective swimming, is still unknown. Here we show that fish can improve their
sustained propulsive efficiency by actively following, and judiciously
intercepting, vortices in the wake of other swimmers. This swimming strategy
leads to collective energy-savings and is revealed through the first ever
combination of deep reinforcement learning with high-fidelity flow simulations.
We find that a `smart-swimmer' can adapt its position and body deformation to
synchronise with the momentum of the oncoming vortices, improving its average
swimming-efficiency at no cost to the leader. The results show that fish may
harvest energy deposited in vortices produced by their peers, and support the
conjecture that swimming in formation is energetically advantageous. Moreover,
this study demonstrates that deep reinforcement learning can produce navigation
algorithms for complex flow-fields, with promising implications for energy
savings in autonomous robotic swarms.

研究发现，鱼在集群游泳时可以利用其同伴产生的涡旋以提高自身的游泳效率，从而实现集体节约能量。同时，本研究还利用深度强化学习与高保真流场模拟相结合的方法，为复杂流场的导航算法提供了新的思路，并为自主机器人的节能提供了良好的应用前景。