arXiv:2608.30946v1 Announce Type: new
Abstract: Closed-loop human-AI systems generate high-dimensional behavioural trajectories whose collective dynamics remain obscure. Using 297,915 learners' adapt...
By Minlin Wu (Tianli Qiming AI Research Institute, Sichuan Qiming Daren Technology Co., Ltd., Chengdu, China), Xu Fang (Tianli Qiming AI Research Institute, Sichuan Qiming Daren Technology Co., Ltd., Chengdu, China), Yicheng Zhang (Swiss AI Laboratories, Blonay, Switzerland), Chenyu Zhou (Tianli Qiming AI Research Institute, Sichuan Qiming Daren Technology Co., Ltd., Chengdu, China), Zhiyi Liu (Tianli Qiming AI Research Institute, Sichuan Qiming Daren Technology Co., Ltd., Chengdu, China)
The paper introduces a world model that learns to predict the evolution of physical systems while respecting key physical principles. By hard‑coding a general structure—generating dynamics from the gradient of a learned energy via a fixed reversible operator and imposing constraints on energy, dissipation, and interventions—the model achieves second‑law compatible dissipation, accurate responses to parameter changes, long‑term stability, and robustness to disturbances. Experiments on an electromagnetic cavity, a particle‑in‑cell grid, and shallow‑water fluid demonstrate that the model can recover accurate constitutive functions, distinguish conserving from dissipating regimes, and transfer learned physics to unseen conditions, outperforming unconstrained models.
By Yufeng Wang, Parivesh Priye, Lu Wei, Haibin Ling
arXiv:2606. 16076v1 Announce Type: cross Abstract: Multivariate forecasting in physical systems requires models that predict coupled temporal variables while preserving meaningful state evolution.
By Weizhi Nie, Weichao Liu, Honglin Guo, Yuting Su
arXiv:2608. 02662v1 Announce Type: cross Abstract: Reliable forecasting of nonlinear physical systems underpins scientific discovery and engineering decision-making.
By Farbod Faraji, Francesco Belardinelli
Autoregressive transformers trained on limited trajectories of nonlinear dynamical systems can extrapolate to unseen parameter regimes, reproducing period-doubling cascades, chaotic dynamics, and attractor structures with high fidelity. In the logistic map, the model captures successive period doublings up to period 128, achieving a scaling ratio within $5 imes10^{-4}$ of the Feigenbaum constant. The study also shows how control‑parameter information is processed via attention, shaping the closed‑loop dynamics during training.
By Yilun Liu, Yi Zhang, Ganyu Wu, Sikuan Yan, Mengyue Wang, Alois Knoll, Volker Tresp, Yunpu Ma
arXiv:2606.19101v2 Announce Type: replace-cross
Abstract: We investigate a structure-first approach to dynamical learning in which the organization of stateful interactions is prescribed explicitly r...
By Augusto Sarti