FPPF learns a conditional flow-matching proposal that approximates the optimal particle-filter proposal, retains exact importance weights, and with localization outperforms classical and generative DA baselines on chaotic systems.
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URL https://www.sciencedirect.com/ science/article/pii/0094576577900960
6 Pith papers cite this work, alongside 1,531 external citations. Polarity classification is still indexing.
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representative citing papers
Manifold-adapted anisotropic radial basis functions, shaped by clustering, yield a global explicit non-intrusive reduced vector field that recovers chaotic invariant measures competitively with intrusive and neural models.
A quantum reservoir network using GHZ-state preparation achieves an order-of-magnitude RMSE improvement over prior QRN designs on latent-space prediction of the Kuramoto-Sivashinsky equation.
Evolutionary selection on reservoir size, connectivity, spectral radius, input scaling, and regularization for Kuramoto-Sivashinsky forecasting reveals a conserved stochastic-block-model spectral envelope, locked intermediate modularity, and a horizontal cost-modularity floor in elite architectures.
A new framework selects suboptimal delay embeddings via combinatorial optimization on in-sample error and combines forecasts to outperform prior methods on toy and flood datasets.
Data-driven equation discovery applied to liquid film flows identifies identifiability issues from multi-collinearity in monomial bases and early-time transients with large residuals.
citing papers explorer
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Generative Model Proposal based Particle Filtering for Data Assimilation
FPPF learns a conditional flow-matching proposal that approximates the optimal particle-filter proposal, retains exact importance weights, and with localization outperforms classical and generative DA baselines on chaotic systems.
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Manifold-adapted radial basis functions for reduced-order modelling of chaotic flows
Manifold-adapted anisotropic radial basis functions, shaped by clustering, yield a global explicit non-intrusive reduced vector field that recovers chaotic invariant measures competitively with intrusive and neural models.
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Leveraging Metrologically Useful States in Quantum Reservoir Networks
A quantum reservoir network using GHZ-state preparation achieves an order-of-magnitude RMSE improvement over prior QRN designs on latent-space prediction of the Kuramoto-Sivashinsky equation.
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Evolutionary Optimization Reveals Structural Constraints on Reservoir Architecture for Spatiotemporal Chaos
Evolutionary selection on reservoir size, connectivity, spectral radius, input scaling, and regularization for Kuramoto-Sivashinsky forecasting reveals a conserved stochastic-block-model spectral envelope, locked intermediate modularity, and a horizontal cost-modularity floor in elite architectures.
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Forecasting high-dimensional dynamics exploiting suboptimal embeddings
A new framework selects suboptimal delay embeddings via combinatorial optimization on in-sample error and combines forecasts to outperform prior methods on toy and flood datasets.
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Data-Driven Equation Discovery for Nonlinear Liquid Film Flows
Data-driven equation discovery applied to liquid film flows identifies identifiability issues from multi-collinearity in monomial bases and early-time transients with large residuals.