A variational moment-nudging framework for Vlasov-Poisson particle-in-cell simulations cuts velocity and temperature errors by up to two orders of magnitude, with a global well-posedness proof for a linear collision surrogate.
Sparse model selection via integral terms.Physical Review E, 96(2):023302
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A measure-based nudging framework assimilates smoothed macroscopic observations into microscopic mean-field particle dynamics using Wasserstein transport velocities on probability measures.
EqOD combines symmetry-based library reduction with stability selection to reach F1=1.000 on several noisy PDE identification tasks where prior methods fail.
citing papers explorer
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Wasserstein Moment Nudging for Vlasov-Poisson Data Assimilation
A variational moment-nudging framework for Vlasov-Poisson particle-in-cell simulations cuts velocity and temperature errors by up to two orders of magnitude, with a global well-posedness proof for a linear collision surrogate.
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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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EqOD: Symmetry-Informed Stability Selection for PDE Identification
EqOD combines symmetry-based library reduction with stability selection to reach F1=1.000 on several noisy PDE identification tasks where prior methods fail.
- MVNN: A Measure-Valued Neural Network for Learning McKean-Vlasov Dynamics from Particle Data