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GeoCFM is introduced, a conditional flow-matching model that generates mineral occurrence point sets conditioned on multi-channel geo-images that improves geometric agreement with observed occurrences over score-map and non-conditional baselines, while representing epistemic uncertainty through conditional sampling.
A physics-inspired neural architecture that decomposes latent-state evolution into dedicated advection, diffusion, and reaction operators and achieves competitive deterministic forecast skill, with particularly strong performance at short to medium lead times, while preserving improved spectral fidelity and forecast ac...
This work explores image generation using flow matching using flow matching and proposes an iterative process that can be integrated into virtually any generative modeling technique, thereby enhancing the performance and robustness of image synthesis systems.
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