LagrangeBench: A Lagrangian Fluid Mechanics Benchmarking Suite
- URL: http://arxiv.org/abs/2309.16342v2
- Date: Sat, 28 Oct 2023 10:58:58 GMT
- Title: LagrangeBench: A Lagrangian Fluid Mechanics Benchmarking Suite
- Authors: Artur P. Toshev, Gianluca Galletti, Fabian Fritz, Stefan Adami,
Nikolaus A. Adams
- Abstract summary: We present LagrangeBench, the first benchmarking suite for Lagrangian particle problems.
Our contribution is: (a) seven new fluid mechanics datasets (four in 2D and three in 3D) generated with the Smoothed Particle Hydrodynamics (SPH) method.
We also go beyond established position errors and introduce physical metrics like kinetic energy MSE and Sinkhorn distance for the particle distribution.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: 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 .
Related papers
- PhyMPGN: Physics-encoded Message Passing Graph Network for spatiotemporal PDE systems [31.006807854698376]
We propose a new graph learning approach, namely, Physics-encoded Message Passing Graph Network (PhyMPGN)
We incorporate a GNN into a numerical integrator to approximate the temporal marching of partialtemporal dynamics for a given PDE system.
PhyMPGN is capable of accurately predicting various types of operatortemporal dynamics on coarse unstructured meshes.
arXiv Detail & Related papers (2024-10-02T08:54:18Z) - Towards Universal Mesh Movement Networks [13.450178050669964]
We introduce the Universal Mesh Movement Network (UM2N)
UM2N can be applied in a non-intrusive, zero-shot manner to move meshes with different size distributions and structures.
We evaluate our method on advection and Navier-Stokes based examples, as well as a real-world tsunami simulation case.
arXiv Detail & Related papers (2024-06-29T09:35:12Z) - JAX-SPH: A Differentiable Smoothed Particle Hydrodynamics Framework [8.977530522693444]
Particle-based fluid simulations have emerged as a powerful tool for solving the Navier-Stokes equations.
Recent addition of machine learning methods to the toolbox for solving such problems is pushing the boundary of the quality vs. speed tradeoff.
We lead the way to Lagrangian fluid simulators compatible with deep learning frameworks, and propose JAX-SPH.
arXiv Detail & Related papers (2024-03-07T18:53:53Z) - Neural SPH: Improved Neural Modeling of Lagrangian Fluid Dynamics [10.420017109857765]
Smoothed particle hydrodynamics (SPH) is omnipresent in modern engineering and scientific disciplines.
Due to the particle-like nature of the simulation, graph neural networks (GNNs) have emerged as appealing and successful surrogates.
In this work, we identify particle clustering originating from tensile instabilities as one of the primary pitfalls.
arXiv Detail & Related papers (2024-02-09T09:40:12Z) - PAC-NeRF: Physics Augmented Continuum Neural Radiance Fields for
Geometry-Agnostic System Identification [64.61198351207752]
Existing approaches to system identification (estimating the physical parameters of an object) from videos assume known object geometries.
In this work, we aim to identify parameters characterizing a physical system from a set of multi-view videos without any assumption on object geometry or topology.
We propose "Physics Augmented Continuum Neural Radiance Fields" (PAC-NeRF), to estimate both the unknown geometry and physical parameters of highly dynamic objects from multi-view videos.
arXiv Detail & Related papers (2023-03-09T18:59:50Z) - 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) - Learning to Solve PDE-constrained Inverse Problems with Graph Networks [51.89325993156204]
In many application domains across science and engineering, we are interested in solving inverse problems with constraints defined by a partial differential equation (PDE)
Here we explore GNNs to solve such PDE-constrained inverse problems.
We demonstrate computational speedups of up to 90x using GNNs compared to principled solvers.
arXiv Detail & Related papers (2022-06-01T18:48:01Z) - Physics Informed RNN-DCT Networks for Time-Dependent Partial
Differential Equations [62.81701992551728]
We present a physics-informed framework for solving time-dependent partial differential equations.
Our model utilizes discrete cosine transforms to encode spatial and recurrent neural networks.
We show experimental results on the Taylor-Green vortex solution to the Navier-Stokes equations.
arXiv Detail & Related papers (2022-02-24T20:46:52Z) - Fast Gravitational Approach for Rigid Point Set Registration with
Ordinary Differential Equations [79.71184760864507]
This article introduces a new physics-based method for rigid point set alignment called Fast Gravitational Approach (FGA)
In FGA, the source and target point sets are interpreted as rigid particle swarms with masses interacting in a globally multiply-linked manner while moving in a simulated gravitational force field.
We show that the new method class has characteristics not found in previous alignment methods.
arXiv Detail & Related papers (2020-09-28T15:05:39Z) - Neural Vortex Method: from Finite Lagrangian Particles to Infinite
Dimensional Eulerian Dynamics [16.563723810812807]
We propose a novel learning-based framework, the Neural Vortex Method (NVM)
NVM builds a neural-network description of the Lagrangian vortex structures and their interaction dynamics.
By embedding these two networks with a vorticity-to-velocity Poisson solver, we can predict the accurate fluid dynamics.
arXiv Detail & Related papers (2020-06-07T15:12:25Z) - Learning to Simulate Complex Physics with Graph Networks [68.43901833812448]
We present a machine learning framework and model implementation that can learn to simulate a wide variety of challenging physical domains.
Our framework---which we term "Graph Network-based Simulators" (GNS)--represents the state of a physical system with particles, expressed as nodes in a graph, and computes dynamics via learned message-passing.
Our results show that our model can generalize from single-timestep predictions with thousands of particles during training, to different initial conditions, thousands of timesteps, and at least an order of magnitude more particles at test time.
arXiv Detail & Related papers (2020-02-21T16:44:28Z)
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.