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Learning Composable Energy Surrogates for PDE Order Reduction

Neural Information Processing Systems

To address this, we leverage parametric modular structure to learn component-level surrogates, enabling cheaper high-fidelity simulation. We use a neural network to model the stored potential energy in a component given boundary conditions.


Leveraging Early-Stage Robustness in Diffusion Models for Efficient and High-Quality Image Synthesis

Neural Information Processing Systems

While diffusion models have demonstrated exceptional image generation capabilities, the iterative noise estimation process required for these models is compute-intensive and their practical implementation is limited by slow sampling speeds.