超越压缩：为神经代理求解器的稳定长时 rollout 训练潜表示

Computer Science > Machine Learning arXiv:2609.30198v1 (cs) [Submitted on 24 Sep 2026] Title:Beyond Compression: Training Latent Representations for Stable Long-Horizon Rollout in Neural Surrogate Solvers Authors:Andreas E. Robertson, Ashley T. Lenau, John D. Shimanek, Benjamin A. Jasperson, Vivek Oommen, David L. Damm, Krishna Garikipati, Remi Dingreville View a PDF of the paper titled Beyond Compression: Training Latent Representations for Stable Long-Horizon Rollout in Neural Surrogate Solvers, by Andreas E. Robertson and 7 other authors View PDF HTML (experimental) Abstract:Latent neural surrogate solvers, or latent dynamics models, accelerate simulations of time-dependent physical systems by evolving a compressed latent space rather than resolving full-resolution fields directly. In principle this reduces computational cost and simplifies learning, but in practice errors often accumulate rapidly during long autoregressive rollouts, limiting predictive utility. We show that this instability does not stem from the latent representation itself, but arises when it is trained solely for reconstruction, producing representations poorly suited to long-horizon forecasting. We systematically evaluate training-level interventions that align latent representations with long-horizon rollout: Koopman operator learning and Hamming noise injection during autoencoder training to improve compression, together with noise injection and multi-step rollout fine-tuning to improve dynamics. Interventions that improve long-horizon rollout stability often degrade conventional training metrics, including reconstruction and one-step prediction accuracy. Collectively, these interventions reduce long-rollout error by approximately 40\% and match or exceed the accuracy of full-resolution models on two physics benchmarks, while requiring 2 orders of magnitude fewer floating point operations and half the GPU memory. Applied to mesoscale crystal-plasticity simulations of high-cycle fatigue, the resulting surrogate achieves stable extrapolation over horizons orders of magnitude beyond those observed during training. More broadly, these results show that neural compression should be designed not merely to reduce dimensionality, but to restructure the solution space for stable dynamical evolution, a key requirement for reliable, efficient neural surrogates in scientific applications. Subjects: Machine Learning (cs.LG); Materials Science (cond-mat.mtrl-sci); Computational Engineering, Finance, and Science (cs.CE) Cite as: arXiv:2609.30198 [cs.LG] (or arXiv:2609.30198v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2609.30198 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Remi Dingreville [view email] [v1] Thu, 24 Sep 2026 17:36:46 UTC (10,376 KB) Full-text links: Access Paper: View a PDF of the paper titled Beyond Compression: Training Latent Representations for Stable Long-Horizon Rollout in Neural Surrogate Solvers, by Andreas E. Robertson and 7 other authors View PDF HTML (experimental) TeX Source view license Current browse context: cs.LG new | recent | 2026-09 Change to browse by: cond-mat cond-mat.mtrl-sci cs cs.CE References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... 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