Flow matching on time series targets a closed-form nonparametric velocity field that is a similarity-weighted mixture of observed transition velocities, making neural models approximations to an ideal memory-augmented dynamical system sampler.
org/abs/2003.02236
3 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2026 3verdicts
UNVERDICTED 3representative citing papers
Koopman autoencoders with forcings and temporal unrolling deliver accurate year-long predictions for coastal-ocean models at 300-1400x speedup, outperforming POD in two of three cases.
A non-intrusive framework combines Koopman autoencoders with a spatio-temporal surrogate to learn and predict physics-constrained dynamics of systems like 2D flow around a cylinder for unseen conditions.
citing papers explorer
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Is Flow Matching Just Trajectory Replay for Sequential Data?
Flow matching on time series targets a closed-form nonparametric velocity field that is a similarity-weighted mixture of observed transition velocities, making neural models approximations to an ideal memory-augmented dynamical system sampler.
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Reduced-Order Surrogates for Forced Flexible Mesh Coastal-Ocean Models
Koopman autoencoders with forcings and temporal unrolling deliver accurate year-long predictions for coastal-ocean models at 300-1400x speedup, outperforming POD in two of three cases.
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Non-intrusive Learning of Physics-Informed Spatio-temporal Surrogate for Accelerating Design
A non-intrusive framework combines Koopman autoencoders with a spatio-temporal surrogate to learn and predict physics-constrained dynamics of systems like 2D flow around a cylinder for unseen conditions.