Author

J. Afonso

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Open access Oct 2026

Physics consistent conditional flow matching model as a plasma physics surrogate model

Physics-informed surrogate modeling for steady-state systems often struggles with data scarcity and optimization instabilities from soft penalty losses. Unlike prevailing physics-informed generative methods that rely on explicit, differentiable PDE residuals during training, our framework targets systems where closed-f...

J. Afonso, Pedro Viegas, Rodrigo Ventura et al. · Machine Learning: Science an... · 0 citations

#artificial intelligence Preprint Oct 2026

Representation Learning via Composite Subspace Flow Matching

Distilling semantic meaning from raw image data remains a challenge in machine learning. Current self-supervised paradigms face a trade-off: joint-embedding architectures produce relevant features by aggressively discarding intra-class variance, irreversibly spatial and reconstruction. Conversely, generative models pre...

J. Afonso, Mário A. T. Figueiredo · 0 citations