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：能够建模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。我们提出一种基于学习的粒子模拟器，采用单一Transformer架构，用于模拟布料、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。模型采用预测-校正设计，基于共享的拉格朗日粒子表示。显式预测器首先在已知外力作用下推进粒子，生成反映外部驱动运动但未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差位置与速度更新：粒子分词器编码局部粒子-粒子、粒子-边界及拓扑引导的相互作用；超分词编码器通过交替的自注意力与分词合并，将粒子分词层次化合并为紧凑的超分词集合；超分词解码器通过交叉注意力将这些超分词提升回粒子分辨率，预测每个粒子的位置与速度校正。渐进式分词合并通过每层将分词数量减半，降低了逐层编码器的注意力开销；解码器则通过紧凑的超分词集合而非全粒子间注意力进行通信。在六类动力学范畴内，同一架构泛化至未见过的材料、边界构型、初始条件及外力。我们进一步展示了下游的交互式控制、逆向设计以及从真实世界操控数据中学习的能力，从而减少了对特定现象求解器工程设计的需求。