Conditional Denoising Model as a Physical Surrogate Model

• URL: http://arxiv.org/abs/2601.21021v1
• Date: Wed, 28 Jan 2026 20:32:20 GMT
• Title: Conditional Denoising Model as a Physical Surrogate Model
• Authors: José Afonso, Pedro Viegas, Rodrigo Ventura, Vasco Guerra,
• Abstract summary: We introduce a generative model designed to learn the geometry of the physical manifold itself.<n>By training the network to restore clean states from noisy ones, the model learns a vector field that points continuously towards the valid solution subspace.
• Score: 1.0616273526777913
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Surrogate modeling for complex physical systems typically faces a trade-off between data-fitting accuracy and physical consistency. Physics-consistent approaches typically treat physical laws as soft constraints within the loss function, a strategy that frequently fails to guarantee strict adherence to the governing equations, or rely on post-processing corrections that do not intrinsically learn the underlying solution geometry. To address these limitations, we introduce the {Conditional Denoising Model (CDM)}, a generative model designed to learn the geometry of the physical manifold itself. By training the network to restore clean states from noisy ones, the model learns a vector field that points continuously towards the valid solution subspace. We introduce a time-independent formulation that transforms inference into a deterministic fixed-point iteration, effectively projecting noisy approximations onto the equilibrium manifold. Validated on a low-temperature plasma physics and chemistry benchmark, the CDM achieves higher parameter and data efficiency than physics-consistent baselines. Crucially, we demonstrate that the denoising objective acts as a powerful implicit regularizer: despite never seeing the governing equations during training, the model adheres to physical constraints more strictly than baselines trained with explicit physics losses.

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