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：能够模拟多种物理现象而无需针对特定求解器重新设计的统一模拟器，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，它采用单一的Transformer架构来模拟布料、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。我们的模型遵循基于共享拉格朗日粒子表示的预测-校正设计。显式预测器首先在已知外力作用下推进粒子，生成一个捕获外力驱动运动但不包含粒子间相互作用的中间状态。然后，一个学习型校正器通过三个阶段预测残余的位置和速度更新：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；超标记编码器，通过交替的自注意力与标记合并，将粒子标记层次化地合并成紧凑的超标记集合；以及超标记解码器，通过交叉注意力将这些超标记还原为粒子分辨率，以预测每个粒子的位置和速度校正。渐进式标记合并通过在每个层级将标记数量减半，降低了后续编码器层的注意力成本，而解码器则通过紧凑的超标记集（而非全粒子对粒子的注意力）进行通信。在六类动力学中，相同的架构能够泛化到未见过的材料、边界配置、初始条件和外力。我们进一步展示了下游的交互控制、逆向设计以及从真实世界操作数据中学习的能力，从而减少了对特定现象求解器工程的需求。