Approximation and Controllability of Nonlinear Control-Affine Systems via Semiautonomous Neural Ordinary Differential Equations | Paper Citation Record

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This paper cites Neural ordinary differential equations.Advances in neural information process- ing systems, 31, 2018.

Approximation and Controllability of Nonlinear Control-Affine Systems via Semiautonomous Neural Ordinary Differential Equations Neural ordinary differential equations.Advances in neural information process- ing systems, 31, 2018

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This paper cites A proposal on machine learning via dynamical systems.Links, 2024:08–27, 2017.

Approximation and Controllability of Nonlinear Control-Affine Systems via Semiautonomous Neural Ordinary Differential Equations A proposal on machine learning via dynamical systems.Links, 2024:08–27, 2017

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Approximation and Controllability of Nonlinear Control-Affine Systems via Semiautonomous Neural Ordinary Differential Equations Augmented neural odes

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Approximation and Controllability of Nonlinear Control-Affine Systems via Semiautonomous Neural Ordinary Differential Equations Deep learning: An introduction for applied mathematicians.Siam review, 61(4):860–891, 2019

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Approximation and Controllability of Nonlinear Control-Affine Systems via Semiautonomous Neural Ordinary Differential Equations Stable architectures for deep neural networks

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Approximation and Controllability of Nonlinear Control-Affine Systems via Semiautonomous Neural Ordinary Differential Equations Cambridge University Press, 2022

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This paper cites Interpolation, approx- imation, and controllability of deep neural networks.SIAM Journal on Control and Optimization, 63(1):625–649, 2025.

Approximation and Controllability of Nonlinear Control-Affine Systems via Semiautonomous Neural Ordinary Differential Equations Interpolation, approx- imation, and controllability of deep neural networks.SIAM Journal on Control and Optimization, 63(1):625–649, 2025

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This paper cites Neural ode control for classification, approximation, and transport.SIAM Review, 65(3):735–773, 2023.

Approximation and Controllability of Nonlinear Control-Affine Systems via Semiautonomous Neural Ordinary Differential Equations Neural ode control for classification, approximation, and transport.SIAM Review, 65(3):735–773, 2023

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Approximation and Controllability of Nonlinear Control-Affine Systems via Semiautonomous Neural Ordinary Differential Equations Universal Approximation Property of Neural Ordinary Differential Equations

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This paper cites Constructive interpolation and generalization rates for neural ODEs: a control perspective.

Approximation and Controllability of Nonlinear Control-Affine Systems via Semiautonomous Neural Ordinary Differential Equations Constructive interpolation and generalization rates for neural ODEs: a control perspective

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Approximation and Controllability of Nonlinear Control-Affine Systems via Semiautonomous Neural Ordinary Differential Equations Neural ode control for trajectory approximation of continuity equation

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This paper cites Universal approxi- mation of dynamical systems by semiautonomous neural odes and applications.

Approximation and Controllability of Nonlinear Control-Affine Systems via Semiautonomous Neural Ordinary Differential Equations Universal approxi- mation of dynamical systems by semiautonomous neural odes and applications

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This paper cites Learning on manifolds: Universal approximations properties using geometric controllability conditions for neural odes.

Approximation and Controllability of Nonlinear Control-Affine Systems via Semiautonomous Neural Ordinary Differential Equations Learning on manifolds: Universal approximations properties using geometric controllability conditions for neural odes

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This paper cites Springer Science & Business Media, 2013.

Approximation and Controllability of Nonlinear Control-Affine Systems via Semiautonomous Neural Ordinary Differential Equations Springer Science & Business Media, 2013

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Approximation and Controllability of Nonlinear Control-Affine Systems via Semiautonomous Neural Ordinary Differential Equations Coron.Control and Nonlinearity

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Approximation and Controllability of Nonlinear Control-Affine Systems via Semiautonomous Neural Ordinary Differential Equations Slotine and W

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This paper cites URLhttps://books.google.co.in/books?id= cwpRAAAAMAAJ.

Approximation and Controllability of Nonlinear Control-Affine Systems via Semiautonomous Neural Ordinary Differential Equations URLhttps://books.google.co.in/books?id= cwpRAAAAMAAJ

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This paper cites Neuralcontrolled differential equations for irregular time series.Advances in neural information processing systems, 33:6696–6707, 2020.

Approximation and Controllability of Nonlinear Control-Affine Systems via Semiautonomous Neural Ordinary Differential Equations Neuralcontrolled differential equations for irregular time series.Advances in neural information processing systems, 33:6696–6707, 2020

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This paper cites Hamiltonian neural networks.Advances in neural information processing systems, 32, 2019.

Approximation and Controllability of Nonlinear Control-Affine Systems via Semiautonomous Neural Ordinary Differential Equations Hamiltonian neural networks.Advances in neural information processing systems, 32, 2019

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This paper cites Neural operator: 45 Learning maps between function spaces with applications to pdes.Journal of Machine Learning Research, 24(89):1–97, 2023.

Approximation and Controllability of Nonlinear Control-Affine Systems via Semiautonomous Neural Ordinary Differential Equations Neural operator: 45 Learning maps between function spaces with applications to pdes.Journal of Machine Learning Research, 24(89):1–97, 2023

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This paper cites Interplay between depth and width for interpolation in neural odes.Neural Networks, 180:106640, 2024.

Approximation and Controllability of Nonlinear Control-Affine Systems via Semiautonomous Neural Ordinary Differential Equations Interplay between depth and width for interpolation in neural odes.Neural Networks, 180:106640, 2024

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This paper cites Generalization bounds for neural ordinary differential equations and deep residual networks.Advances in neural information processing systems, 36:48918–48938, 2023.

Approximation and Controllability of Nonlinear Control-Affine Systems via Semiautonomous Neural Ordinary Differential Equations Generalization bounds for neural ordinary differential equations and deep residual networks.Advances in neural information processing systems, 36:48918–48938, 2023

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This paper cites Deep neural networks, generic universal interpolation, and controlled odes.SIAM Journal on Mathe- matics of Data Science, 2(3):901–919, 2020.

Approximation and Controllability of Nonlinear Control-Affine Systems via Semiautonomous Neural Ordinary Differential Equations Deep neural networks, generic universal interpolation, and controlled odes.SIAM Journal on Mathe- matics of Data Science, 2(3):901–919, 2020

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Approximation and Controllability of Nonlinear Control-Affine Systems via Semiautonomous Neural Ordinary Differential Equations Universal approximation bounds for superpositions of a sigmoidal function.IEEE Transactions on Information theory, 39(3):930–945, 2002

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Approximation and Controllability of Nonlinear Control-Affine Systems via Semiautonomous Neural Ordinary Differential Equations Approximation theory of the mlp model in neural networks.Acta numerica, 8:143–195, 1999

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Approximation and Controllability of Nonlinear Control-Affine Systems via Semiautonomous Neural Ordinary Differential Equations Solving high-dimensional partial differential equations using deep learning.Proceedings of the National Academy of Sciences, 115(34):8505–8510, 2018

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Approximation and Controllability of Nonlinear Control-Affine Systems via Semiautonomous Neural Ordinary Differential Equations Optimal approximation of zonoids and uniform approxima- tion by shallow neural networks.Constructive Approximation, 62(2):441–469, 2025

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Approximation and Controllability of Nonlinear Control-Affine Systems via Semiautonomous Neural Ordinary Differential Equations Sharp bounds on the approximation rates, metric entropy, and n-widths of shallow neural networks.Foundations of Com- putational Mathematics, 24(2):481–537, 2024

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Approximation and Controllability of Nonlinear Control-Affine Systems via Semiautonomous Neural Ordinary Differential Equations Neural operators for accelerating sci- entific simulations and design.Nature Reviews Physics, 6(5):320–328, 2024

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Approximation and Controllability of Nonlinear Control-Affine Systems via Semiautonomous Neural Ordinary Differential Equations Laplace neural operator for solving differential equations.Nature Machine Intelligence, 6(6): 631–640, 2024

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Approximation and Controllability of Nonlinear Control-Affine Systems via Semiautonomous Neural Ordinary Differential Equations Spectral op- erator learning for parametric pdes without data reliance.Computer Methods in Applied Mechanics and Engineering, 420:116678, 2024

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Approximation and Controllability of Nonlinear Control-Affine Systems via Semiautonomous Neural Ordinary Differential Equations Neural operators for adaptive control of freeway traffic.Automatica, 182:112553, 2025

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Approximation and Controllability of Nonlinear Control-Affine Systems via Semiautonomous Neural Ordinary Differential Equations Improved gener- alization with deep neural operators for engineering systems: Path towards dig- ital twin.Engineering Applications of Artificial Intelligence, 131:107844, 2024

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Approximation and Controllability of Nonlinear Control-Affine Systems via Semiautonomous Neural Ordinary Differential Equations Deep neural operator-driven real-time inference to enable digital twin solutions for nuclear energy systems

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Approximation and Controllability of Nonlinear Control-Affine Systems via Semiautonomous Neural Ordinary Differential Equations The admm-pinns algorith- mic framework for nonsmooth pde-constrained optimization: a deep learning approach.SIAM Journal on Scientific Computing, 46(6):C659–C687, 2024

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Approximation and Controllability of Nonlinear Control-Affine Systems via Semiautonomous Neural Ordinary Differential Equations The hard-constraint pinns for interface optimal control problems.SIAM Journal on Scientific Computing, 47(3):C601–C629, 2025

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Approximation and Controllability of Nonlinear Control-Affine Systems via Semiautonomous Neural Ordinary Differential Equations Respecting causality for training physics-informed neural networks.Computer Methods in Applied Me- chanics and Engineering, 421:116813, 2024

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Approximation and Controllability of Nonlinear Control-Affine Systems via Semiautonomous Neural Ordinary Differential Equations Control of neural transport for nor- malising flows.Journal de Mathématiques Pures et Appliquées, 181:58–90, 2024

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Approximation and Controllability of Nonlinear Control-Affine Systems via Semiautonomous Neural Ordinary Differential Equations Deep residual learning for image recog- nition: A survey.Applied sciences, 12(18):8972, 2022

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Approximation and Controllability of Nonlinear Control-Affine Systems via Semiautonomous Neural Ordinary Differential Equations Number 106 in CBMS Regional Conference Series in Mathematics

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Approximation and Controllability of Nonlinear Control-Affine Systems via Semiautonomous Neural Ordinary Differential Equations The barron space and the flow-induced function spaces for neural network models.Constructive Approximation, 55(1):369–406, 2022

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Approximation and Controllability of Nonlinear Control-Affine Systems via Semiautonomous Neural Ordinary Differential Equations Klusowski and Andrew R

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