A measure-based nudging framework assimilates smoothed macroscopic observations into microscopic mean-field particle dynamics using Wasserstein transport velocities on probability measures.
Propagation of chaos: a review of models, methods and applications
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MVNN learns measure-dependent drift terms in McKean-Vlasov equations from particle data using an embedding network, with proofs of well-posedness, propagation of chaos, and universal approximation under low-dimensional assumptions.
The paper establishes sharp relative entropy estimates for marginals of non-exchangeable interacting particle systems by linking a BBGKY hierarchy to first-passage percolation.
citing papers explorer
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Multiscale Nudging: From Macroscopic Observations to Microscopic Dynamics
A measure-based nudging framework assimilates smoothed macroscopic observations into microscopic mean-field particle dynamics using Wasserstein transport velocities on probability measures.
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MVNN: A Measure-Valued Neural Network for Learning McKean-Vlasov Dynamics from Particle Data
MVNN learns measure-dependent drift terms in McKean-Vlasov equations from particle data using an embedding network, with proofs of well-posedness, propagation of chaos, and universal approximation under low-dimensional assumptions.
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Quantitative propagation of chaos for non-exchangeable diffusions via first-passage percolation
The paper establishes sharp relative entropy estimates for marginals of non-exchangeable interacting particle systems by linking a BBGKY hierarchy to first-passage percolation.