SNAP-FM accelerates nonlinear constraint projection in Physics-Constrained Flow Matching by exploiting block-sparse Jacobian and KKT structures with ExaModels.jl, MadNLP.jl, and GPU sparse factorization on PDE benchmarks.
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LASER couples a latent world model with a GRPO-trained RL policy to adaptively reposition sensors for sparse continuum field reconstruction, outperforming fixed and offline-optimized placement strategies across PDE and real-world benchmarks.
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SNAP-FM: Sparse Nonlinear Accelerated Projection for Physics-Constrained Generative Modeling
SNAP-FM accelerates nonlinear constraint projection in Physics-Constrained Flow Matching by exploiting block-sparse Jacobian and KKT structures with ExaModels.jl, MadNLP.jl, and GPU sparse factorization on PDE benchmarks.
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LASER: Learning Active Sensing for Continuum Field Reconstruction
LASER couples a latent world model with a GRPO-trained RL policy to adaptively reposition sensors for sparse continuum field reconstruction, outperforming fixed and offline-optimized placement strategies across PDE and real-world benchmarks.