WorldParticle: Unified World Simulation of Lagrangian Particle Dynamics via Transformer

• URL: http://arxiv.org/abs/2605.15305v4
• Date: Wed, 20 May 2026 14:25:31 GMT
• Title: WorldParticle: Unified World Simulation of Lagrangian Particle Dynamics via Transformer
• Abstract summary: 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.
• Score: 107.36949516442782
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: 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.

Related papers

• DyG$^2$T: Modeling Object Dynamics with 3D Gaussian Temporal-Spatial Particle Graph Transformer [86.4618122245946]
  DyG$2$T is a dynamics modeling framework that infers object motion trajectories by spatially completing and temporally discriminating Key Point representations.<n>Experiments on both synthetic and real-world datasets demonstrate that DyG$2$T achieves accurate dynamics modeling and reasoning.
  arXiv  Detail & Related papers  (2026-08-19T03:44:28Z)
• PhysiFormer: Learning to Simulate Mechanics in World Space [56.16644631495]
  We present PhysiFormer, a diffusion transformer for physically-plausible 3D object motion.<n>It represents objects as 3D meshes expressed in world coordinates.<n>It substantially outperforms autoregressive baselines in trajectory accuracy, rigidity preservation, and momentum-based physical consistency.
  arXiv  Detail & Related papers  (2026-06-25T17:59:00Z)
• BRICKS: Compositional Neural Markov Kernels for Zero-Shot Radiation-Matter Simulation [2.2650024890957385]
  We introduce a new strategy for compositional neural surrogates for radiation-matter interactions.<n>Exploiting the locality and the Markov nature of particle interactions, we create a emphnext-particle prediction kernel.<n>The model generates variable-sized typed sets of particles and radiation side effects that are the result of the interaction of an incident particle with a material volume.
  arXiv  Detail & Related papers  (2026-05-07T17:19:31Z)
• Engineering quantum criticality and dynamics on an analog-digital simulator [26.76525389323686]
  We use coherent mapping between the Rydberg and hyperfine qubits in a neutral atom array simulator to engineer and probe complex quantum dynamics.<n>We first demonstrate dynamical engineering of ring-exchange and particle hopping dynamics via Floquet driving.<n>We employ closed-loop optimization to target an out-of-equilibrium critical quantum spin liquid of the Rokhsar-Kivelson type.
  arXiv  Detail & Related papers  (2026-02-20T19:00:00Z)
• Neural Particle Automata: Learning Self-Organizing Particle Dynamics [28.914778832424066]
  We introduce Particle Automata (NPA), a Lagrangian generalization of Neural Cellular Automata (NCA)<n>NPA model each cell as a particle with a continuous position and internal state, both updated by a shared, learnable neural rule.<n>We show that NPA retain key NCA behaviors such as robustness and self-regeneration, while enabling new behaviors specific to particle systems.
  arXiv  Detail & Related papers  (2026-01-22T16:46:28Z)
• From Black Hole to Galaxy: Neural Operator: Framework for Accretion and Feedback Dynamics [70.27068115318681]
  We introduce a neural-based ''subgrid black hole'' that learns the small-scale local dynamics and embeds it within direct simulations.<n>Thanks to the great speedup in fine-scale evolution, our approach captures intrinsic variability in accretion-driven feedback, allowing dynamic coupling between the central black hole and galaxy-scale gas.
  arXiv  Detail & Related papers  (2025-12-01T11:47:49Z)
• OmniSAT: Compact Action Token, Faster Auto Regression [70.70037017501357]
  We introduce an Omni Swift Action Tokenizer, which learns a compact, transferable action representation.<n>The resulting discrete tokenization shortens the training sequence by 6.8$times$, and lowers the target entropy.
  arXiv  Detail & Related papers  (2025-10-08T03:55:24Z)
• Simulating Schwinger model dynamics with quasi-one-dimensional qubit arrays [0.0]
  We develop a strategy to run Schwinger model dynamics on synthetic quantum spin lattices. We show that global magnetic field patterns can drive coherent quantum dynamics of the interface equivalent to the lattice Schwinger Hamiltonian. This work opens up a path for near-term quantum simulators to address questions of immediate relevance to particle physics.
  arXiv  Detail & Related papers  (2024-09-22T17:58:25Z)
• Interpretable Joint Event-Particle Reconstruction for Neutrino Physics at NOvA with Sparse CNNs and Transformers [124.29621071934693]
  We present a novel neural network architecture that combines the spatial learning enabled by convolutions with the contextual learning enabled by attention. TransformerCVN simultaneously classifies each event and reconstructs every individual particle's identity. This architecture enables us to perform several interpretability studies which provide insights into the network's predictions.
  arXiv  Detail & Related papers  (2023-03-10T20:36:23Z)
• Transformer with Implicit Edges for Particle-based Physics Simulation [135.77656965678196]
  Transformer with Implicit Edges (TIE) captures the rich semantics of particle interactions in an edge-free manner. We evaluate our model on diverse domains of varying complexity and materials.
  arXiv  Detail & Related papers  (2022-07-22T03:45:29Z)
• Graph Neural Network for Hamiltonian-Based Material Property Prediction [56.94118357003096]
  We present and compare several different graph convolution networks that are able to predict the band gap for inorganic materials. The models are developed to incorporate two different features: the information of each orbital itself and the interaction between each other. The results show that our model can get a promising prediction accuracy with cross-validation.
  arXiv  Detail & Related papers  (2020-05-27T13:32:10Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.