{"ID":2855784,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2510.16004","arxiv_id":"2510.16004","title":"PAINT: Parallel-in-time Neural Twins for Dynamical System Reconstruction","abstract":"Neural surrogates have shown great potential in simulating dynamical systems, while offering real-time capabilities. We envision Neural Twins as a progression of neural surrogates, aiming to create digital replicas of real systems. A neural twin consumes measurements at test time to update its state, thereby enabling context-specific decision-making. We argue, that a critical property of neural twins is their ability to remain on-trajectory, i.e., to stay close to the true system state over time. We introduce Parallel-in-time Neural Twins (PAINT), an architecture-agnostic family of methods for modeling dynamical systems from measurements. PAINT trains a generative neural network to model the distribution of states in parallel over time. At test time, states are predicted from measurements in a sliding window fashion. Our theoretical analysis shows that PAINT is on-trajectory, whereas autoregressive models generally are not. Empirically, we evaluate our method on a challenging two-dimensional turbulent fluid dynamics problem. The results demonstrate that PAINT stays on-trajectory and predicts system states from sparse measurements with high fidelity. These findings underscore PAINT's potential for developing neural twins that stay on-trajectory, enabling more accurate state estimation and decision-making.","short_abstract":"Neural surrogates have shown great potential in simulating dynamical systems, while offering real-time capabilities. We envision Neural Twins as a progression of neural surrogates, aiming to create digital replicas of real systems. A neural twin consumes measurements at test time to update its state, thereby enabling c...","url_abs":"https://arxiv.org/abs/2510.16004","url_pdf":"https://arxiv.org/pdf/2510.16004v2","authors":"[\"Andreas Radler\",\"Vincent Seyfried\",\"Johannes Brandstetter\",\"Thomas Lichtenegger\"]","published":"2025-10-14T14:22:45Z","proceeding":"cs.AI","tasks":"[\"cs.AI\",\"physics.flu-dyn\"]","methods":"[]","has_code":false}