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建模多样物理现象的统一模拟器，是模拟科学领域长期追求的目标。本文提出一种基于学习的粒子模拟器，其构建于单一Transformer架构之上，可模拟布料、弹性固体、牛顿流体与非牛顿流体、颗粒材料及分子动力学。我们的模型采用预测-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，生成中间状态，该状态捕获了外力驱动的运动，但未包含粒子间相互作用。随后，一个学习型校正器通过三个阶段预测剩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-边界及拓扑引导的相互作用；超级分词编码器，通过交替自注意力与分词合并，将粒子分词层次化地合并为紧凑的超级分词集合；以及超级分词解码器，通过交叉注意力将这些超级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。渐进式分词合并通过每层将分词数量减半，降低了后续编码器层的注意力成本，而解码器通过紧凑的超级分词集合进行通信，而非全粒子到粒子的注意力。在六类动力学中，同一架构可泛化至未见过的材料、边界配置、初始条件及外力。我们进一步展示了下游的交互控制、逆向设计及从真实世界操作数据中学习的能力，从而减少了针对每种现象进行求解器工程的需求。