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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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.
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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.