Paper Citation Record · LEDGER

Weight-Parameterization in Continuous Time Deep Neural Networks for Surrogate Modeling

As of 12 August 2026, this Paper Citation Record lists 34 of 34 outbound references and 0 inbound Pith citation observations for arXiv:2507.22045.

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pith.paper-citation-record.v1
2507.22045 v1

Coverage vector

measured 34 of 34 reference resolution

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Reference resolution

34 of 34 outbound references displayed

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External citation measurements

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Outbound references

Observation 654b486e-05b0-4ee3-9720-e4e5824d55c6 · outbound

This paper cites In: Summer School of the German Research School for Simula tion Sciences (2019).

Weight-Parameterization in Continuous Time Deep Neural Networks for Surrogate Modeling In: Summer School of the German Research School for Simula tion Sciences (2019)

Reference 1

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This paper cites : Tgcnn: An eﬃcient surrogate for real-time data assimila- tion in subsurface ﬂow.

Weight-Parameterization in Continuous Time Deep Neural Networks for Surrogate Modeling : Tgcnn: An eﬃcient surrogate for real-time data assimila- tion in subsurface ﬂow

Reference 2

Resolution: verified exact; doi, observed 2026-08-06T12:10:08.773820Z

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Observation 88976fa9-7954-404a-9c31-7fde41b5f522 · outbound

This paper cites The Innovat ion Energy 2(2), 100087–1 (2025).

Weight-Parameterization in Continuous Time Deep Neural Networks for Surrogate Modeling The Innovat ion Energy 2(2), 100087–1 (2025)

Reference 3

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Observation 7d7a7a24-1d05-4d5e-902c-5544d46b5f8a · outbound

This paper cites Journal of Computationa l physics 378, 686–707 (2019).

Weight-Parameterization in Continuous Time Deep Neural Networks for Surrogate Modeling Journal of Computationa l physics 378, 686–707 (2019)

Reference 4

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Observation 49b925bf-4eef-4350-93fb-b640eb4e7ad6 · outbound

This paper cites Nature machine intelligence 3(3), 218–229 (2021).

Weight-Parameterization in Continuous Time Deep Neural Networks for Surrogate Modeling Nature machine intelligence 3(3), 218–229 (2021)

Reference 5

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Observation 3f3401cb-70d8-4799-9bc8-d45257348eb1 · outbound

This paper cites Neural Ordinary Differential Equations.

Weight-Parameterization in Continuous Time Deep Neural Networks for Surrogate Modeling Neural Ordinary Differential Equations

Reference 6

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Weight-Parameterization in Continuous Time Deep Neural Networks for Surrogate Modeling Unresolved cited work

Reference 7

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Observation afaad01e-ae60-4d30-b6b2-48def51172bb · outbound

This paper cites Spline parameterization of neural network controls for deep learning.

Weight-Parameterization in Continuous Time Deep Neural Networks for Surrogate Modeling Spline parameterization of neural network controls for deep learning

Reference 8

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This paper cites Communications in Mathematics and Statistics 5(1), 1–11 (2017) https://doi.org/10.1007/s40304-017-0103-z.

Weight-Parameterization in Continuous Time Deep Neural Networks for Surrogate Modeling Communications in Mathematics and Statistics 5(1), 1–11 (2017) https://doi.org/10.1007/s40304-017-0103-z

Reference 9

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Observation c9d39d8b-b8aa-4439-a697-e2f43d671190 · outbound

This paper cites Stable Architectures for Deep Neural Networks.

Weight-Parameterization in Continuous Time Deep Neural Networks for Surrogate Modeling Stable Architectures for Deep Neural Networks

Reference 10

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Observation f59a1069-c747-4bb2-9f6a-46d461b7acec · outbound

This paper cites ResNet After All? Neural ODEs and Their Numerical Solution.

Weight-Parameterization in Continuous Time Deep Neural Networks for Surrogate Modeling ResNet After All? Neural ODEs and Their Numerical Solution

Reference 11

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Observation ce438e1c-c00f-4ea0-aedd-641905424e4e · outbound

This paper cites Do Residual Neural Networks discretize Neural Ordinary Differential Equations?.

Weight-Parameterization in Continuous Time Deep Neural Networks for Surrogate Modeling Do Residual Neural Networks discretize Neural Ordinary Differential Equations?

Reference 12

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Observation 950c966e-6e01-4632-be33-a0cb33007488 · outbound

This paper cites Time Dependence in Non-Autonomous Neural ODEs.

Weight-Parameterization in Continuous Time Deep Neural Networks for Surrogate Modeling Time Dependence in Non-Autonomous Neural ODEs

Reference 13

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Observation 66e64d08-9fd5-491d-8030-68ce641e288f · outbound

This paper cites Deep Learning via Dynamical Systems: An Approximation Perspective.

Weight-Parameterization in Continuous Time Deep Neural Networks for Surrogate Modeling Deep Learning via Dynamical Systems: An Approximation Perspective

Reference 14

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Observation 13a65930-487c-4792-8d97-120bb8bd13d4 · outbound

This paper cites Journal of Computational Dynamics 6(2), 171–198 (2019).

Weight-Parameterization in Continuous Time Deep Neural Networks for Surrogate Modeling Journal of Computational Dynamics 6(2), 171–198 (2019)

Reference 15

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Observation 16dc8dff-7d75-454f-b320-9e9a3424d0ec · outbound

This paper cites Train Like a (Var)Pro: Efficient Training of Neural Networks with Variable Projection.

Weight-Parameterization in Continuous Time Deep Neural Networks for Surrogate Modeling Train Like a (Var)Pro: Efficient Training of Neural Networks with Variable Projection

Reference 16

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Observation 8a78efac-de59-4223-8bfd-fb6377572ea1 · outbound

This paper cites Symplectic ODE-Net: Learning Hamiltonian Dynamics with Control.

Weight-Parameterization in Continuous Time Deep Neural Networks for Surrogate Modeling Symplectic ODE-Net: Learning Hamiltonian Dynamics with Control

Reference 17

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Observation c4300a36-0fe6-4f18-9e4d-df0c14e9ad37 · outbound

This paper cites In: ICLR 2024 Workshop on AI4Diﬀeren tialEquations In Science (2024).

Weight-Parameterization in Continuous Time Deep Neural Networks for Surrogate Modeling In: ICLR 2024 Workshop on AI4Diﬀeren tialEquations In Science (2024)

Reference 18

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Observation 6b9f9dc7-6acb-4248-8f30-e32ad15da889 · outbound

This paper cites Machine Learning: Science and Technology 6(2), 025069 (2025) https://doi.org/10.1088/2632-2153/ade4ee.

Weight-Parameterization in Continuous Time Deep Neural Networks for Surrogate Modeling Machine Learning: Science and Technology 6(2), 025069 (2025) https://doi.org/10.1088/2632-2153/ade4ee

Reference 19

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Observation 37ce1318-7d2e-45a4-a64a-4422625f729e · outbound

This paper cites Computer Methods in Applied Mec hanics and Engineering 441, 117990 (2025) https://doi.org/10.1016/j.cma.2025.117990.

Weight-Parameterization in Continuous Time Deep Neural Networks for Surrogate Modeling Computer Methods in Applied Mec hanics and Engineering 441, 117990 (2025) https://doi.org/10.1016/j.cma.2025.117990

Reference 20

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Observation a514f1e5-da69-4016-9a17-b7c1aecf12f8 · outbound

This paper cites Neural Generalized Ordinary Differential Equations with Layer-varying Parameters.

Weight-Parameterization in Continuous Time Deep Neural Networks for Surrogate Modeling Neural Generalized Ordinary Differential Equations with Layer-varying Parameters

Reference 21

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Observation da7a2da1-fa9e-41bb-9b8e-ed69a7df16bf · outbound

This paper cites Dissecting Neural ODEs.

Weight-Parameterization in Continuous Time Deep Neural Networks for Surrogate Modeling Dissecting Neural ODEs

Reference 22

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Observation acee1843-3d4e-464e-ab66-b720726a33e4 · outbound

This paper cites Deep Residual Learning for Image Recognition.

Weight-Parameterization in Continuous Time Deep Neural Networks for Surrogate Modeling Deep Residual Learning for Image Recognition

Reference 23

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Observation 812f2ca8-4789-4eae-b8ba-4c2ce9767915 · outbound

This paper cites Orthogonal Weight Normalization: Solution to Optimization over Multiple Dependent Stiefel Manifolds in Deep Neural Networks.

Weight-Parameterization in Continuous Time Deep Neural Networks for Surrogate Modeling Orthogonal Weight Normalization: Solution to Optimization over Multiple Dependent Stiefel Manifolds in Deep Neural Networks

Reference 24

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Observation 6ccee5cc-2e9a-4e8a-a2be-5066adf487a6 · outbound

This paper cites On orthogonality and learning recurrent networks with long term dependencies.

Weight-Parameterization in Continuous Time Deep Neural Networks for Surrogate Modeling On orthogonality and learning recurrent networks with long term dependencies

Reference 25

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This paper cites Discretize-Optimize vs. Optimize-Discretize for Time-Series Regression and Continuous Normalizing Flows.

Weight-Parameterization in Continuous Time Deep Neural Networks for Surrogate Modeling Discretize-Optimize vs. Optimize-Discretize for Time-Series Regression and Continuous Normalizing Flows

Reference 26

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This paper cites In: Leitmann, G.

Weight-Parameterization in Continuous Time Deep Neural Networks for Surrogate Modeling In: Leitmann, G

Reference 27

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This paper cites ANODE: Unconditionally Accurate Memory-Efficient Gradients for Neural ODEs.

Weight-Parameterization in Continuous Time Deep Neural Networks for Surrogate Modeling ANODE: Unconditionally Accurate Memory-Efficient Gradients for Neural ODEs

Reference 28

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Observation acc3c3e3-e5b8-4314-a4b1-6a4089165d60 · outbound

This paper cites Applied Mathematical Sciences.

Weight-Parameterization in Continuous Time Deep Neural Networks for Surrogate Modeling Applied Mathematical Sciences

Reference 29

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Observation b2d31095-021e-4f88-bcba-58b5641222cb · outbound

This paper cites Lipschitz Flow-box Theorem.

Weight-Parameterization in Continuous Time Deep Neural Networks for Surrogate Modeling Lipschitz Flow-box Theorem

Reference 30

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Weight-Parameterization in Continuous Time Deep Neural Networks for Surrogate Modeling Unresolved cited work

Reference 31

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This paper cites YouTube, NeurIPS 2020 Workshop on Diﬀerentiable Programming (2 020).

Weight-Parameterization in Continuous Time Deep Neural Networks for Surrogate Modeling YouTube, NeurIPS 2020 Workshop on Diﬀerentiable Programming (2 020)

Reference 32

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Observation ae733367-69ca-4b7d-836b-969d62cbc46d · outbound

This paper cites Adam: A Method for Stochastic Optimization.

Weight-Parameterization in Continuous Time Deep Neural Networks for Surrogate Modeling Adam: A Method for Stochastic Optimization

Reference 33

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This paper cites In: Pattern Recognition and Computer Vision: Se c- ond Chinese Conference, PRCV 2019, Xi’an, China, November 8-11, 2019, Proceedings, Part I, pp.

Weight-Parameterization in Continuous Time Deep Neural Networks for Surrogate Modeling In: Pattern Recognition and Computer Vision: Se c- ond Chinese Conference, PRCV 2019, Xi’an, China, November 8-11, 2019, Proceedings, Part I, pp

Reference 34

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