研究人员正在开发新颖的神经网络架构，以更好地模拟复杂的物理动力学。一种方法RO-HNN结合了哈密顿力学和模型降阶，以处理高维系统并强制执行守恒定律。另一种方法侧重于为无序的微观状态学习置换不变表示，从而能够准确预测粒子相互作用和聚合物拉伸等系统中的宏观动力学。此外，一项研究探讨了量子物理中高斯态的高效哈密顿学习，利用外差测量和局部反演技术以对数样本复杂度推断参数。 AI
影响 这些进展可能在从流体动力学到量子物理等领域带来更准确、更高效的模拟。
排序理由 多篇arXiv上发表的研究论文详细介绍了用于模拟物理动力学的新AI方法。
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arXiv:2509.24627v2 Announce Type: replace Abstract: Embedding physical intuition into network architectures allows the learning of dynamics that enforce fundamental properties, such as energy conservation laws, thereby leading to physically-plausible predictions. Yet, scaling the…
arXiv:2411.03163v4 Announce Type: replace-cross Abstract: In this work, we initiate the study of Hamiltonian learning for positive temperature bosonic Gaussian states, the quantum generalization of the widely studied problem of learning Gaussian graphical models. We obtain effici…
arXiv:2605.30812v1 Announce Type: new Abstract: Accurately modeling the macroscopic dynamics of high-dimensional microscopic systems is of broad interest across the sciences. Many data-driven approaches learn a low-dimensional latent state through an autoencoder trained for point…