Abstract:Hyperbolic conservation laws govern a wide range of transport-driven dynamics featuring shocks, contact discontinuities, and complex wave interactions, posing distinct challenges for deep-learning-based surrogate modeling. While classical numerical methods provide robust and physically admissible solutions, their computational cost restricts applicability in many-query tasks such as parametric studies and design optimization. Conversely, existing neural surrogates offer rapid inference but often fail to respect intrinsic PDE structures, leading to non-physical artifacts, rollout instability, and poor generalization.
We present an interpretable, structure-preserving graph neural solver that bridges classical numerical principles with graph neural networks (GNNs). The network is designed as a learned reconstruction-and-flux operator rather than a black-box state updater, thereby inherently preserving key properties such as local conservation and upwinding. Inspired by Arbitrary high-order DERivatives schemes, we further recast message-passing GNNs as high-order space-time predictors, enabling conservative and stable neural updates with large time steps.
Evaluation is performed on challenging supersonic flow benchmarks spanning broad parametric variations in geometry, initial/boundary conditions, and flow regimes. The neural solver achieves superior long-horizon rollout stability and accuracy compared with strong surrogate baselines, outperforms low-order discretizations, and delivers orders-of-magnitude runtime speedups over high-resolution simulations.
| Subjects: | Computational Physics (physics.comp-ph); Machine Learning (cs.LG); Numerical Analysis (math.NA) |
| Cite as: | arXiv:2604.15617 [physics.comp-ph] |
| (or arXiv:2604.15617v1 [physics.comp-ph] for this version) | |
| https://doi.org/10.48550/arXiv.2604.15617 arXiv-issued DOI via DataCite (pending registration) |
From: Jiamin Jiang [view email]
[v1]
Fri, 17 Apr 2026 01:45:54 UTC (10,357 KB)
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