MASF redesigns the forward diffusion process to align with measurements, yielding a theoretically grounded likelihood score and up to 28.2x speedup on O(10^5)-dimensional Kolmogorov flow under sparse and nonlinear observation operators.
Dmitrii Kochkov, Jamie A
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Adversarial optimal transport objectives train neural emulators with improved long-term statistical fidelity on chaotic systems.
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Rethinking Forward Processes for Score-Based Nonlinear Data Assimilation in High Dimensions
MASF redesigns the forward diffusion process to align with measurements, yielding a theoretically grounded likelihood score and up to 28.2x speedup on O(10^5)-dimensional Kolmogorov flow under sparse and nonlinear observation operators.
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Learning to Emulate Chaos: Adversarial Optimal Transport Regularization
Adversarial optimal transport objectives train neural emulators with improved long-term statistical fidelity on chaotic systems.