泛音：用于清洁、高效和灵活物理模拟器的循环补丁调制
Overtone: Cyclic Patch Modulation for Clean, Efficient, and Flexible Physics Emulators
AI总结：
Overtone通过动态补丁大小控制，在保持精度的同时提升计算效率，适用于复杂物理模拟场景。
摘要：
基于变换器的PDE代理在性能方面取得了显著成果，但面临两个关键挑战：固定补丁大小导致在谐波频率上系统性误差累积，以及计算成本在问题复杂度或可用资源变化时保持不变。我们介绍了Overtone，通过推理过程中的动态补丁大小控制提供统一解决方案。Overtone的核心思想是通过在自回归滚动过程中循环调节补丁大小，将误差分布到频谱中，从而缓解固定补丁模型中系统性谐波伪影累积的问题。我们通过两个架构无关的模块——CSM（使用动态步长调节）和CKM（使用动态核大小调节）来实现这一目标，这两个模块共同提供了谐波缓解和计算适应性部署。这种灵活的标记化使用户可以根据计算约束动态地在精度和速度之间进行权衡，而循环滚动策略在方差归一化的RMSE（VRMSE）指标上相比传统固定补丁代理可降低高达40%的长滚动误差。在具有挑战性的2D和3D PDE基准测试中，一个Overtone模型在推理计算预算下，当在固定总训练预算设置下训练时，可以匹配或超过固定补丁基线。
英文摘要：
Transformer-based PDE surrogates achieve remarkable performance but face two key challenges: fixed patch sizes cause systematic error accumulation at harmonic frequencies, and computational costs remain inflexible regardless of problem complexity or available resources. We introduce Overtone, a unified solution through dynamic patch size control at inference. Overtone's key insight is that cyclically modulating patch sizes during autoregressive rollouts distributes errors across the frequency spectrum, mitigating the systematic harmonic artifact accumulation that plague fixed-patch models. We implement this through two architecture-agnostic modules--CSM (using dynamic stride modulation) and CKM (using dynamic kernel resizing)--that together provide both harmonic mitigation and compute-adaptive deployment. This flexible tokenization lets users trade accuracy for speed dynamically based on computational constraints, and the cyclic rollout strategy yields up to 40% lower long rollout error in variance-normalised RMSE (VRMSE) compared to conventional, static-patch surrogates. Across challenging 2D and 3D PDE benchmarks, one Overtone model matches or exceeds fixed-patch baselines across inference compute budgets, when trained under a fixed total training budget setting.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
