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.
AIChE Journal38, 10 (1992), 1499–1511
4 Pith papers cite this work. Polarity classification is still indexing.
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Holomorphic neural networks enforce exact satisfaction of harmonic PDEs for 3D Laplace and elasticity problems using Whittaker representations and boundary-only training.
Domain experts require fast convergence and some explainability from evolutionary algorithms in physics-informed optimization, with other needs varying by problem, revealing an application gap.
This perspective paper categorizes hybrid architectures for combining mechanistic and data-driven models using residual learning, Neural ODEs, and solver-in-the-loop to model neurological disorder progression.
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Integrating Mechanistic and Data-Driven Models for Neurological Disorders through Differentiable Programming
This perspective paper categorizes hybrid architectures for combining mechanistic and data-driven models using residual learning, Neural ODEs, and solver-in-the-loop to model neurological disorder progression.