Communication Dans Un Congrès Année : 2024

AROMA: Preserving Spatial Structure for Latent PDE Modeling with Local Neural Fields

Résumé

We present AROMA (Attentive Reduced Order Model with Attention), a framework designed to enhance the modeling of partial differential equations (PDEs) using local neural fields. Our flexible encoder-decoder architecture can obtain smooth latent representations of spatial physical fields from a variety of data types, including irregular-grid inputs and point clouds. This versatility eliminates the need for patching and allows efficient processing of diverse geometries. The sequential nature of our latent representation can be interpreted spatially and permits the use of a conditional transformer for modeling the temporal dynamics of PDEs. By employing a diffusion-based formulation, we achieve greater stability and enable longer rollouts compared to conventional MSE training. AROMA's superior performance in simulating 1D and 2D equations underscores the efficacy of our approach in capturing complex dynamical behaviors.

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Dates et versions

hal-04761005 , version 1 (01-09-2025)

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Louis Serrano, Thomas X Wang, Etienne Le Naour, Jean-Noël Vittaut, Patrick Gallinari. AROMA: Preserving Spatial Structure for Latent PDE Modeling with Local Neural Fields. Advances in Neural Information Processing Systems 37 (NeurIPS 2024), Dec 2024, Vancouver, Canada. ⟨10.48550/arXiv.2406.02176⟩. ⟨hal-04761005⟩
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