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.
citing papers explorer
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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.
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A holomorphic neural network framework for 3D boundary value problems governed by harmonic potentials
Holomorphic neural networks enforce exact satisfaction of harmonic PDEs for 3D Laplace and elasticity problems using Whittaker representations and boundary-only training.
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Performance and Explainability Requirements of Evolutionary Algorithms in Real-World Physics-Informed Optimization
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.
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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.