mlsanityData to 7 Oct 2026

LagrangeBench: A Lagrangian Fluid Mechanics Benchmarking Suite

arXiv 2309.16342 ↗First posted 28 Sep 2023cs.LG physics.flu-dyncited by 26

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Abstract (arXiv)

Machine learning has been successfully applied to grid-based PDE modeling in various scientific applications. However, learned PDE solvers based on Lagrangian particle discretizations, which are the preferred approach to problems with free surfaces or complex physics, remain largely unexplored. We present LagrangeBench, the first benchmarking suite for Lagrangian particle problems, focusing on temporal coarse-graining. In particular, our contribution is: (a) seven new fluid mechanics datasets (four in 2D and three in 3D) generated with the Smoothed Particle Hydrodynamics (SPH) method including the Taylor-Green vortex, lid-driven cavity, reverse Poiseuille flow, and dam break, each of which includes different physics like solid wall interactions or free surface, (b) efficient JAX-based API with various recent training strategies and three neighbor search routines, and (c) JAX implementation of established Graph Neural Networks (GNNs) like GNS and SEGNN with baseline results. Finally, to measure the performance of learned surrogates we go beyond established position errors and introduce physical metrics like kinetic energy MSE and Sinkhorn distance for the particle distribution. Our codebase is available at https://github.com/tumaer/lagrangebench .

Key facts. Cited by 26 papers in the map (data through 7 Oct 2026). Builds on 32 of its 91 references that are in the map. First posted on arXiv 28 Sep 2023.

Builds on

32 of 91 references.

References · 32

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  1. 2019Graph Transformer Networkscited by 520
  2. 2016Interaction Networks for Learning about Objects, Relations and Physicscited by 503
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  4. 2021Equivariant message passing for the prediction of tensorial properties and molecular spectracited by 438
  5. 2022PDEBENCH: An Extensive Benchmark for Scientific Machine Learningcited by 396
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 9; 2025: 7; 2026: 10.

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  1. 2024The Well: a Large-Scale Collection of Diverse Physics Simulations for Machine Learningcited by 134
  2. 2024DrivAerNet++: A Large-Scale Multimodal Car Dataset with Computational Fluid Dynamics Simulations and Deep Learning Benchmarkscited by 74
  3. 2024Universal Physics Transformers: A Framework For Efficiently Scaling Neural Operatorscited by 25
  4. 2024Active Learning for Neural PDE Solverscited by 22
  5. 2025Curly Flow Matching for Learning Non-gradient Field Dynamicscited by 17
  6. 2026RealPDEBench: A Benchmark for Complex Physical Systems with Real-World Datacited by 13
  7. 2024Neural SPH: Improved Neural Modeling of Lagrangian Fluid Dynamicscited by 10
  8. 2025SIMSHIFT: A Benchmark for Adapting Neural Surrogates to Distribution Shiftscited by 6
  9. 2025EngiBench: A Framework for Data-Driven Engineering Design Researchcited by 6
  10. 2025DrivAerStar: An Industrial-Grade CFD Dataset for Vehicle Aerodynamic Optimizationcited by 6
  11. 2024A Neural Material Point Method for Particle-based Emulationcited by 5
  12. 2024A Cosmic-Scale Benchmark for Symmetry-Preserving Data Processingcited by 5
14 more
  1. 2024JAX-SPH: A Differentiable Smoothed Particle Hydrodynamics Frameworkcited by 3
  2. 2026Emergent Transfer of a Physics Foundation Model from Simulation to Laboratory Turbulencecited by 3
  3. 2025AneumoBench: A Source-Linked Benchmark for Synthetic-Geometry Transfer in Aneurysm CFDcited by 2
  4. 2026PDEInvBench: A Comprehensive Dataset and Design Space Exploration of Neural Networks for PDE Inverse Problemscited by 2
  5. 2025CORGI: GNNs with Convolutional Residual Global Interactions for Lagrangian Simulationcited by 1
  6. 2026Response Renormalization for Critical Deep Equilibrium Modelscited by 1
  7. 2024ConDiff: A Challenging Dataset for Neural Solvers of Partial Differential Equationscited by 0
  8. 2025PLAID: A Unified Data Model for Machine Learning on Heterogeneous Physics Simulationscited by 0
  9. 2026Stabilizing Test-Time Adaptation of High-Dimensional Simulation Surrogates via D-Optimal Statisticscited by 0
  10. 2026CarCrashNet: A Large-Scale Dataset and Hierarchical Neural Solver for Data-Driven Structural Crash Simulationcited by 0
  11. 2026BubbleSH: A Dataset of Rising Bubbles with Deformable Interfacescited by 0
  12. 2026Generalized Neural Operator for Parametric and Boundary-Value Problemscited by 0
  13. 2026Hybrid Lagrangian-Eulerian Model for Lagrangian Fluid Simulationcited by 0
  14. 2026From Fixed Grids to Moving Particles:A Transferable Latent Operator for Fluid Dynamicscited by 0