1 citations · 1 across the 6 of their papers we have counts for
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Composing Flow-Matching Energies with Known Physics: Generation, OOD Detection, and Inversion on PDE Fields
Yixuan Sun, Anirban Samaddar, Sandeep Madireddy
Probabilistic modeling of physical fields benefits from both a data-driven prior and known physical structure such as the governing equations. Energy-based models (EBMs) are a natu…
Uncovering Physical Drivers of Dark Matter Halo Structures with Auxiliary-Variable-Guided Generative Models
Arkaprabha Ganguli, Anirban Samaddar, Florian Kéruzoré +4
Deep generative models (DGMs) compress high-dimensional data but often entangle distinct physical factors in their latent spaces. We present an auxiliary-variable-guided framework…
Multi-task Modeling for Engineering Applications with Sparse Data
Yigitcan Comlek, R. Murali Krishnan, Sandipp Krishnan Ravi +7
Modern engineering and scientific workflows often require simultaneous predictions across related tasks and fidelity levels, where high-fidelity data is scarce and expensive, while…