A grouped pooling strategy with ensemble Kalman inversion improves accuracy of expected information gain estimators in Bayesian experimental design at amortized computational cost.
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Hybrid NODE retains mechanistic kinetics for free-radical polymerization and learns only the radical concentration closure, achieving RMSE 0.013 on noisy unseen conditions versus 0.31 and 0.68 for data-driven baselines with as few as ten measurements.
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Hybrid Neural Ordinary Differential Equations for Data-Efficient Polymerization Modeling with Incomplete Kinetics
Hybrid NODE retains mechanistic kinetics for free-radical polymerization and learns only the radical concentration closure, achieving RMSE 0.013 on noisy unseen conditions versus 0.31 and 0.68 for data-driven baselines with as few as ten measurements.