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An Energy-Based Conservative-Dissipative Latent Neural Evolution Operator for Magnetization Dynamics

2026-09-07 12:00 Science 🔥 40.2 heat score

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On September 7, 2026, arXiv cs.LG published a research paper titled “An Energy-Based Conservative-Dissipative Latent Neural Evolution Operator for Magnetization Dynamics”. This study proposes an energy-based conservative-dissipative latent variable neural evolution operator aimed at simulating magnetization dynamics.

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A arXiv cs.LG en 2026-09-07 12:00

An Energy-Based Conservative-Dissipative Latent Neural Evolution Operator for Magnetization Dynamics

研究人员开发了一种基于能量的降阶模型，用于模拟磁化动力学。该模型将卷积自编码器与结构化潜在神经常微分方程耦合，利用学习到的标量势梯度生成潜在向量场。编码器和解码器在短轨迹窗口上联合训练，仅使用潜在和滚动损失，无需时间导数监督或耗散惩罚。推理时仅需一次编码并在潜在空间演化，显著降低了预测成本。研究对比了二次、深度及加性深度二次潜在能量，发现含反对称与耗散项的模型在不间断滚动预测中精度更高；其中深二次能量在两种场方向下整体精度最佳，且误差增长更慢。