Learning Macroscopic Dynamics without Reconstructing Microscopic States
Modeling the temporal evolution of macroscopic properties of complex systems is an important scientific task. To predict this evolution without full microscopic simulation, a common approach encodes microstates into compact latent states, learns their evolution, and reads out macroscopic predictions from the latent trajectory. These latent states are often learned through microstate reconstruction. However, with limited latent capacity, reconstruction can favor high-variance microscopic details over information needed for macroscopic prediction. Yet jointly learning latent states and their transition without reconstruction often fails to obtain latent dynamics that support accurate macros...
| Authors | Zhichao Han, Yue Zhao, Qianxiao Li |
|---|---|
| Primary category | cs.LG |
| Published | 29 September 2026 |
| Last updated at source | 2 October 2026 |
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