A unified simulator that can model diverse physical phenomena without solver-specific redesign is a long-standing goal across simulation science. We present a learning-based particle simulator built on a single transformer architecture to model cloth, elastic solds, Newtonian and non-Newtonian fluids, granular materials, and molecular dynamics. Our model follows a prediction-correction design on a shared Lagrangian particle representation. An explicit predictor first advances particles under the known external forces, producing an intermediate state that captures externally driven motion but not inter-particle interactions. A learned corrector then predicts the residual position and velocity updates through three stages: a particle tokenizer that encodes local particle-particle, particle-boundary, and topology-guided interactions; a super-token encoder that hierarchically merges particle tokens into a compact set of super tokens via alternating self-attention and token merging; and a super-token decoder that lifts these super tokens back to particle resolution through cross-attention to predict per-particle position and velocity corrections. Progressive token merging reduces the attention cost at successive encoder layers by halving the token count at each level, and the decoder communicates through the compact super-token set rather than full particle-to-particle attention. Across the six dynamics categories, the same architecture generalizes to unseen materials, boundary configurations, initial conditions, and external forces. We further demonstrate downstream interactive control, inverse design, and learning from real-world manipulation data, reducing the need for per-phenomenon solver engineering.

翻译：能够在无需针对特定求解器重新设计的情况下模拟多种物理现象的统一模拟器，是模拟科学领域长期追求的目标。我们提出了一种基于学习的粒子模拟器，其采用单一的Transformer架构，可模拟布料、弹性固体、牛顿流体与非牛顿流体、颗粒材料以及分子动力学。我们的模型基于共享的拉格朗日粒子表征，采用预测-校正设计。显式预测器首先在已知外力作用下将粒子向前推进，生成一个捕捉外力驱动运动（但不包含粒子间相互作用）的中间状态。随后，学习型校正器通过三个阶段预测残余的位置与速度更新：粒子分词器，用于编码局部粒子-粒子、粒子-边界以及拓扑引导的相互作用；超分词编码器，通过交替进行自注意力计算与分词合并，将粒子分词分层合并为紧凑的超分词集合；以及超分词解码器，通过交叉注意力机制将这些超分词升采样回粒子分辨率，以预测每个粒子的位置与速度校正。逐步分词合并机制通过在每个编码层将分词数量减半来降低注意力计算成本，而解码器则通过紧凑的超分词集合进行通信，而非在全粒子对粒子注意力机制下运行。在全部六类动力学现象中，相同的架构能够泛化至未见过的材料、边界条件、初始条件及外力情况。我们进一步展示了其在交互式控制、逆向设计以及从真实世界操作数据中学习等方面的下游应用，从而减少了针对不同物理现象进行求解器工程改造的需求。