DynaSolver：面向动态边界 4D 流场预测的神经算子
DynaSolver: A Neural Operator for Autoregressive 4D Flow Prediction with Dynamic Boundaries
机器人动起来周围气流怎么算？这篇用物理 token 加并行训练把动态边界 CFD 预测的自回归误差压下去了，还放出了 RoboCFD-4D 基准，做流体仿真代理的可以看看。
DynaSolver 是一个神经算子，用于处理运动边界（如机器人关节）引起的非定常流场预测，显式条件化于边界演化与因果流历史。作者发布了 RoboCFD-4D，一个覆盖多种机器人形态和流动条件的时间分辨 CFD 基准。方法上采用物理 token 公式化摊销点到 token 的投影计算，并引入 Replay-Augmented Parallel Training 缩小训练与自回归 rollout 的差距。在 RoboCFD-4D 上，DynaSolver 在多类学习型 CFD 代理模型中取得最佳全 rollout 精度，且 rollout 越长优势越明显，表明自回归误差累积被显著降低。
DynaSolver: A Neural Operator for Autoregressive 4D Flow Prediction with Dynamic Boundaries
Neural operators provide efficient surrogates for computational fluid dynamics (CFD), but most are designed for static geometries and are not equipped to model unsteady flows induced by continuously moving boundaries. We study dynamic-boundary 4D CFD prediction for articulated moving geometries and introduce RoboCFD-4D, a temporally resolved robotic CFD benchmark spanning diverse morphologies and flow conditions. To address the computational and autoregressive challenges of this setting, we propose DynaSolver, a neural operator that explicitly conditions on boundary evolution and causal flow history. DynaSolver employs an efficient physical-token formulation that amortizes point-to-token projection across the operator stack, substantially reducing repeated point-space spatio-temporal computation while retaining accurate point-level reconstruction. We further introduce Replay-Augmented Parallel Training, which exposes the model to autoregressively generated histories while preserving parallel dense supervision, narrowing the gap between ground-truth-conditioned training and autoregressive rollout. Experiments on RoboCFD-4D show that DynaSolver achieves the best full-rollout accuracy among a broad range of learned CFD surrogates, with its advantage increasing as rollout proceeds, indicating substantially reduced autoregressive error accumulation. Project page: https://made-gpt.github.io/DynaSolver-ProjectPage.
