FPPF uses a learned conditional generative proposal approximating the optimal proposal in particle filters, with tractable likelihoods for Bayesian updates and localization for high dimensions, outperforming baselines on nonlinear non-Gaussian systems.
Progress of Theoreti- cal Physics Supplement64, 346–367 (1978)
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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.
Quantized local reduced-order models paired with adjoint optimization reconstruct full trajectories in the chaotic Kuramoto-Sivashinsky equation up to 0.25 Lyapunov times with 3.5x speedup over full-order models.
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.
Comparative experiments on three chaotic systems find that architectures using integrator-like updates exhibit lower bias, reduced perturbation amplification, and more stable long-horizon rollouts than other common designs when model capacity is matched.
citing papers explorer
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Generative Model Proposal based Particle Filtering for Data Assimilation
FPPF uses a learned conditional generative proposal approximating the optimal proposal in particle filters, with tractable likelihoods for Bayesian updates and localization for high dimensions, outperforming baselines on nonlinear non-Gaussian systems.
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A comparative study of accuracy and rollout stability of temporal surrogate models
Comparative experiments on three chaotic systems find that architectures using integrator-like updates exhibit lower bias, reduced perturbation amplification, and more stable long-horizon rollouts than other common designs when model capacity is matched.