1 citations · 1 across the 6 of their papers we have counts for
7 papers
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…
Chance-constrained Flow Matching for High-Fidelity Constraint-aware Generation
Jinhao Liang, Yixuan Sun, Anirban Samaddar +2
Generative models excel at synthesizing high-fidelity samples from complex data distributions, but they often violate hard constraints arising from physical laws or task specificat…
Efficient Flow Matching using Latent Variables
Anirban Samaddar, Yixuan Sun, Viktor Nilsson +1
Flow matching models have shown great potential in image generation tasks among probabilistic generative models. However, most flow matching models in the literature do not explici…
REMEDI: Corrective Transformations for Improved Neural Entropy Estimation
Viktor Nilsson, Anirban Samaddar, Sandeep Madireddy +1
Information theoretic quantities play a central role in machine learning. The recent surge in the complexity of data and models has increased the demand for accurate estimation of…
Sparsity-Inducing Categorical Prior Improves Robustness of the Information Bottleneck
Anirban Samaddar, Sandeep Madireddy, Prasanna Balaprakash +3
The information bottleneck framework provides a systematic approach to learning representations that compress nuisance information in the input and extract semantically meaningful…