A multilinear operator learned on PCA coefficients maps time-since-ignition inputs to smoke outputs, matching Monte Carlo accuracy with half the model calls and outperforming prior classifiers on holdout data.
PyApprox: A Software Package for Sensitivity Analysis, Bayesian Inference, Optimal Ex- perimental Design, and Multi-Fidelity Uncertainty Quantification and Surrogate Modeling
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Enabling Real-Time Training of a Wildfire-to-Smoke Map with Multilinear Operators
A multilinear operator learned on PCA coefficients maps time-since-ignition inputs to smoke outputs, matching Monte Carlo accuracy with half the model calls and outperforming prior classifiers on holdout data.