Paper Citation Record · LEDGER

Neural Kolmogorov Equations: Parallelizable Learning of Stochastic Dynamics under General Noise

As of 11 August 2026, this Paper Citation Record lists 49 of 49 outbound references and 0 inbound Pith citation observations for arXiv:2607.19173.

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pith.paper-citation-record.v1
2607.19173 v1

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measured 49 of 49 reference resolution

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Outbound references

Observation bc727a27-f6c4-4642-a425-cd96100bf66f · outbound

This paper cites an unresolved cited work.

Neural Kolmogorov Equations: Parallelizable Learning of Stochastic Dynamics under General Noise Unresolved cited work

Reference 1

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Observation 874494a7-03b9-4b32-b946-b1d7bfed37bf · outbound

This paper cites Universal Differential Equations for Scientific Machine Learning.

Neural Kolmogorov Equations: Parallelizable Learning of Stochastic Dynamics under General Noise Universal Differential Equations for Scientific Machine Learning

Reference 2

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Observation 217d0a04-ebfd-46e0-8711-fe3ca993592b · outbound

This paper cites Cohen, Christoph Reisinger, and Sheng Wang.

Neural Kolmogorov Equations: Parallelizable Learning of Stochastic Dynamics under General Noise Cohen, Christoph Reisinger, and Sheng Wang

Reference 3

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This paper cites FP-Diffusion: Improving score-based diffusion models by enforcing the underlying score Fokker–Planck equation.

Neural Kolmogorov Equations: Parallelizable Learning of Stochastic Dynamics under General Noise FP-Diffusion: Improving score-based diffusion models by enforcing the underlying score Fokker–Planck equation

Reference 4

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Observation 566d4e11-2519-4e93-9ba6-817c13d4c917 · outbound

This paper cites Neural controlled differential equations for irregular time series.

Neural Kolmogorov Equations: Parallelizable Learning of Stochastic Dynamics under General Noise Neural controlled differential equations for irregular time series

Reference 5

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Neural Kolmogorov Equations: Parallelizable Learning of Stochastic Dynamics under General Noise Unresolved cited work

Reference 6

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This paper cites Neural SDEs as infinite-dimensional GANs.

Neural Kolmogorov Equations: Parallelizable Learning of Stochastic Dynamics under General Noise Neural SDEs as infinite-dimensional GANs

Reference 7

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Observation a8e3de92-1447-43a6-833a-b9a9499e425e · outbound

This paper cites an unresolved cited work.

Neural Kolmogorov Equations: Parallelizable Learning of Stochastic Dynamics under General Noise Unresolved cited work

Reference 8

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Observation 30f7ee20-7737-4383-bbf6-f2c66a7dac7d · outbound

This paper cites Denoising diffusion probabilistic models.

Neural Kolmogorov Equations: Parallelizable Learning of Stochastic Dynamics under General Noise Denoising diffusion probabilistic models

Reference 9

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This paper cites Kingma, Abhishek Kumar, Stefano Ermon, and Ben Poole.

Neural Kolmogorov Equations: Parallelizable Learning of Stochastic Dynamics under General Noise Kingma, Abhishek Kumar, Stefano Ermon, and Ben Poole

Reference 10

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Neural Kolmogorov Equations: Parallelizable Learning of Stochastic Dynamics under General Noise Unresolved cited work

Reference 11

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Observation 71eafc62-d4e1-4086-89b7-a07a2edba64b · outbound

This paper cites Kloeden and Eckhard Platen.Numerical Solution of Stochastic Differential Equations.

Neural Kolmogorov Equations: Parallelizable Learning of Stochastic Dynamics under General Noise Kloeden and Eckhard Platen.Numerical Solution of Stochastic Differential Equations

Reference 12

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Observation 6dc0a4d8-cbe8-42f4-abb1-8f09bffaebde · outbound

This paper cites Trajectory flow matching with applications to clinical time series modelling.

Neural Kolmogorov Equations: Parallelizable Learning of Stochastic Dynamics under General Noise Trajectory flow matching with applications to clinical time series modelling

Reference 13

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This paper cites Naesseth.

Neural Kolmogorov Equations: Parallelizable Learning of Stochastic Dynamics under General Noise Naesseth

Reference 14

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This paper cites Neural SDE: Stabilizing Neural ODE Networks with Stochastic Noise.

Neural Kolmogorov Equations: Parallelizable Learning of Stochastic Dynamics under General Noise Neural SDE: Stabilizing Neural ODE Networks with Stochastic Noise

Reference 15

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Neural Kolmogorov Equations: Parallelizable Learning of Stochastic Dynamics under General Noise Unresolved cited work

Reference 16

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This paper cites Neural jump-diffusion temporal point processes.

Neural Kolmogorov Equations: Parallelizable Learning of Stochastic Dynamics under General Noise Neural jump-diffusion temporal point processes

Reference 17

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Neural Kolmogorov Equations: Parallelizable Learning of Stochastic Dynamics under General Noise Unresolved cited work

Reference 18

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This paper cites Convergence and stability analysis of neural stochastic differential equations with poisson jumps.Chaos, Solitons & Fractals, 2026.

Neural Kolmogorov Equations: Parallelizable Learning of Stochastic Dynamics under General Noise Convergence and stability analysis of neural stochastic differential equations with poisson jumps.Chaos, Solitons & Fractals, 2026

Reference 19

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This paper cites Variational inference with normalizing flows.

Neural Kolmogorov Equations: Parallelizable Learning of Stochastic Dynamics under General Noise Variational inference with normalizing flows

Reference 20

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Neural Kolmogorov Equations: Parallelizable Learning of Stochastic Dynamics under General Noise Karniadakis

Reference 21

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This paper cites Williams, Ioannis G.

Neural Kolmogorov Equations: Parallelizable Learning of Stochastic Dynamics under General Noise Williams, Ioannis G

Reference 22

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This paper cites A differential geometric approach to nonlinear filtering: The projection filter.IEEE Transactions on Automatic Control, 43(2):247– 252, 1998.

Neural Kolmogorov Equations: Parallelizable Learning of Stochastic Dynamics under General Noise A differential geometric approach to nonlinear filtering: The projection filter.IEEE Transactions on Automatic Control, 43(2):247– 252, 1998

Reference 23

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Observation c16ccc37-4d6f-47de-9e9c-1ba7d4b17fd2 · outbound

This paper cites Approximate nonlinear filtering by projection on exponential manifolds of densities.Bernoulli, 5(3):495–534, 1999.

Neural Kolmogorov Equations: Parallelizable Learning of Stochastic Dynamics under General Noise Approximate nonlinear filtering by projection on exponential manifolds of densities.Bernoulli, 5(3):495–534, 1999

Reference 24

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Neural Kolmogorov Equations: Parallelizable Learning of Stochastic Dynamics under General Noise Unresolved cited work

Reference 25

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Observation f5f08949-5f21-4939-9b14-f5ad1fe23dc6 · outbound

This paper cites Approximate solution of the Fokker–Planck–Kolmogorov equation.Probabilistic Engineering Mechanics, 17(4):369–384, 2002.

Neural Kolmogorov Equations: Parallelizable Learning of Stochastic Dynamics under General Noise Approximate solution of the Fokker–Planck–Kolmogorov equation.Probabilistic Engineering Mechanics, 17(4):369–384, 2002

Reference 26

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Neural Kolmogorov Equations: Parallelizable Learning of Stochastic Dynamics under General Noise Gingold and Joseph J

Reference 27

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Reference 29

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This paper cites On the construction and comparison of difference schemes.SIAM Journal on Numerical Analysis, 5(3):506–517, 1968.

Neural Kolmogorov Equations: Parallelizable Learning of Stochastic Dynamics under General Noise On the construction and comparison of difference schemes.SIAM Journal on Numerical Analysis, 5(3):506–517, 1968

Reference 30

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Neural Kolmogorov Equations: Parallelizable Learning of Stochastic Dynamics under General Noise Rousseeuw and Christophe Croux

Reference 31

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Reference 32

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This paper cites Neural SDEs as a Unified Approach to Continuous-Domain Sequence Modeling.

Neural Kolmogorov Equations: Parallelizable Learning of Stochastic Dynamics under General Noise Neural SDEs as a Unified Approach to Continuous-Domain Sequence Modeling

Reference 33

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Neural Kolmogorov Equations: Parallelizable Learning of Stochastic Dynamics under General Noise Decoupled Weight Decay Regularization

Reference 34

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Neural Kolmogorov Equations: Parallelizable Learning of Stochastic Dynamics under General Noise American Mathematical Society and Oxford University Press, 2000

Reference 35

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This paper cites Singularity structures and impacts on parameter estimation in finite mixtures of distributions.SIAM Journal on Mathematics of Data Science, 1(4):730–758, 2019.

Neural Kolmogorov Equations: Parallelizable Learning of Stochastic Dynamics under General Noise Singularity structures and impacts on parameter estimation in finite mixtures of distributions.SIAM Journal on Mathematics of Data Science, 1(4):730–758, 2019

Reference 36

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Neural Kolmogorov Equations: Parallelizable Learning of Stochastic Dynamics under General Noise Dempster, Nan M

Reference 37

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This paper cites From molecular dynamics to brownian dynamics.Proceedings.

Neural Kolmogorov Equations: Parallelizable Learning of Stochastic Dynamics under General Noise From molecular dynamics to brownian dynamics.Proceedings

Reference 38

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Reference 39

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Neural Kolmogorov Equations: Parallelizable Learning of Stochastic Dynamics under General Noise Consequently,G K becomes a stratified space rather than a smooth manifold [36]

Reference 40

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Neural Kolmogorov Equations: Parallelizable Learning of Stochastic Dynamics under General Noise Unresolved cited work

Reference 41

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Neural Kolmogorov Equations: Parallelizable Learning of Stochastic Dynamics under General Noise Proof sketch.Permuting Gaussian components leaves the mixture density unchanged

Reference 42

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Reference 43

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Neural Kolmogorov Equations: Parallelizable Learning of Stochastic Dynamics under General Noise Unresolved cited work

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Neural Kolmogorov Equations: Parallelizable Learning of Stochastic Dynamics under General Noise Unresolved cited work

Reference 45

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This paper cites 18 C Lifted Dynamics This appendix provides theoretical justification for the approximations underlying Neural Kolmogorov Equations.

Neural Kolmogorov Equations: Parallelizable Learning of Stochastic Dynamics under General Noise 18 C Lifted Dynamics This appendix provides theoretical justification for the approximations underlying Neural Kolmogorov Equations

Reference 46

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Reference 47

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Reference 48

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This paper cites Throughout this section, we assume f∈C 3 b (Rd,R d), a:=gg ⊤ ∈C 2 b (Rd,R d×d), and that all jump measures possess finite third moments.

Neural Kolmogorov Equations: Parallelizable Learning of Stochastic Dynamics under General Noise Throughout this section, we assume f∈C 3 b (Rd,R d), a:=gg ⊤ ∈C 2 b (Rd,R d×d), and that all jump measures possess finite third moments

Reference 49

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