Deep generative models are promising tools for science and engineering, but their reliance on abundant, high-quality data limits applicability. We present a novel framework for generative modeling of random fields (probability distributions over continuous functions) that incorporates domain knowledge to supplement limited, sparse, and indirect data. The foundation of the approach is latent flow matching, where generative modeling occurs on compressed function representations in the latent space of a pre-trained variational autoencoder (VAE). Innovations include the adoption of a function decoder within the VAE and integration of physical/statistical constraints into the VAE training process. In this way, a latent function representation is learned that yields continuous random field samples satisfying domain-specific constraints when decoded, even in data-limited regimes. Efficacy is demonstrated on two challenging applications: wind velocity field reconstruction from sparse sensors and material property inference from a limited number of indirect measurements. Results show that the proposed framework achieves significant improvements in reconstruction accuracy compared to unconstrained methods and enables effective inference with relatively small training datasets that is intractable without constraints.
View on arXiv@article{warner2025_2505.13007,
title={ Generative Modeling of Random Fields from Limited Data via Constrained Latent Flow Matching },
author={ James E. Warner and Tristan A. Shah and Patrick E. Leser and Geoffrey F. Bomarito and Joshua D. Pribe and Michael C. Stanley },
journal={arXiv preprint arXiv:2505.13007},
year={ 2025 }
}