Learning Mesh-Based Simulation with Graph Networks

Abstract: Mesh-based simulations are central to modeling complex physical systems in many disciplines across science and engineering. Mesh representations support powerful numerical integration methods and their resolution can be adapted to strike favorable trade-offs between accuracy and efficiency. However, high-dimensional scientific simulations are very expensive to run, and solvers and parameters must often be tuned individually to each system studied. Here we introduce MeshGraphNets, a framework for learning mesh-based simulations using graph neural networks. Our model can be trained to pass messages on a mesh graph and to adapt the mesh discretization during forward simulation. Our results show it can accurately predict the dynamics of a wide range of physical systems, including aerodynamics, structural mechanics, and cloth. The model's adaptivity supports learning resolution-independent dynamics and can scale to more complex state spaces at test time. Our method is also highly efficient, running 1-2 orders of magnitude faster than the simulation on which it is trained. Our approach broadens the range of problems on which neural network simulators can operate and promises to improve the efficiency of complex, scientific modeling tasks.

• The paper proposes MeshGraphNets, a framework that learns resolution-independent dynamics through graph neural networks applied to mesh representations.
• It employs an Encode-Process-Decode architecture with adaptive remeshing, using message-passing blocks to model complex physical behaviors.
• The approach achieves simulations up to 10–100 times faster than traditional methods while delivering robust error metrics across various domains.

Learning Mesh-Based Simulation with Graph Networks

Mesh-based simulations are integral to modeling complex physical systems, where mesh representations facilitate powerful numerical approaches to solving PDEs. However, the computational cost of high-dimensional simulations is significant, and fine-tuning is often required. This paper proposes MeshGraphNets, a framework leveraging graph neural networks (GNNs) to learn mesh-based simulations, presenting a novel way to efficiently predict dynamics across various domains such as fluid dynamics and structural mechanics.

MeshGraphNets enable the simulation model to adaptively change mesh discretization during forward simulation, providing both efficiency and accuracy. This adaptability allows the model to learn resolution-independent dynamics, which is crucial for scaling to complex state spaces.

Numerical Results and Claims

The paper presents compelling numerical evidence showcasing that MeshGraphNets perform simulations at speeds one to two orders of magnitude faster than traditional methods. The results indicate its capability to predict dynamics with high fidelity across diverse physical systems, including aerodynamics, structural mechanics, and cloth simulation.

MeshGraphNets exhibit superior performance to particle- and grid-based baselines, demonstrating robust error metrics in numerous experimental domains. Notably, it outperforms general graph convolutional networks (GCNs) and convolutional neural network (CNN)-based approaches by leveraging graph-based message passing and relative encoding.

Methodology

The MeshGraphNet architecture employs an Encode-Process-Decode paradigm:

1. Encoding: Converts mesh representation into a graph, with nodes representing mesh vertices and edges encoding the spatial relationships necessary for simulating internal dynamics.
2. Processing: Involves a sequence of message-passing blocks updating node and edge embeddings, thus capturing the complex physics of the system.
3. Decoding: Translates processed embeddings to predict dynamic quantities like velocities and pressures, used for mesh updates in simulation.

Moreover, the framework supports learned adaptive remeshing, employing a learned sizing field for mesh refinement, demonstrating scalability and generalizability to larger and more complex scenarios than those on which it was initially trained.

Implications and Future Directions

The framework's efficacy implies significant practical advancements for scientific modeling, particularly in fields requiring large-scale and efficient simulations. On a theoretical level, this approach may inspire further integration of machine learning with traditional numerical methods, driving new methodologies for simulating complex systems within constrained computational budgets.

Future developments could focus on further enhancing adaptive mechanisms or applying the model's capabilities to emergent domains such as real-time simulation and reinforcement learning for control tasks. The potential to incorporate domain-specific adjustments or physics-based constraints could also refine its accuracy and applicability.

MeshGraphNets represent a meaningful progression towards faster and more efficient simulations, with the adaptability to scale beyond the training scenarios, offering broad applicability in scientific and engineering tasks.