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Paper Citation Record · LEDGER

Emulating Recombination with Neural Networks using Universal Differential Equations

As of 13 August 2026, this Paper Citation Record lists 57 of 57 outbound references and 0 inbound Pith citation observations for arXiv:2411.15140.

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pith.paper-citation-record.v1
2411.15140 v2

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measured 57 of 57 reference resolution

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57 of 57 outbound references displayed

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Outbound references

Observation 9851dff5-70b4-4d13-a3fa-bc0e854655dd · outbound

This paper cites Planck 2018 results. VI. Cosmological parameters.

Emulating Recombination with Neural Networks using Universal Differential Equations Planck 2018 results. VI. Cosmological parameters

Reference 1

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Observation e55bd3ce-c003-4f13-bac9-9759e12c5846 · outbound

This paper cites Thornton, P.A.R.

Emulating Recombination with Neural Networks using Universal Differential Equations Thornton, P.A.R

Reference 2

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Observation 6b9f4f49-3080-4d80-806d-e63bd2d979d5 · outbound

This paper cites SPT-3G: A Next-Generation Cosmic Microwave Background Polarization Experiment on the South Pole Telescope.

Emulating Recombination with Neural Networks using Universal Differential Equations SPT-3G: A Next-Generation Cosmic Microwave Background Polarization Experiment on the South Pole Telescope

Reference 3

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Observation 9fa5d081-925e-4bc7-9bb2-f27a102bb33a · outbound

This paper cites CLASS: The Cosmology Large Angular Scale Surveyor.

Emulating Recombination with Neural Networks using Universal Differential Equations CLASS: The Cosmology Large Angular Scale Surveyor

Reference 4

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Observation 6c51bc39-162f-4d63-b537-9958932298d5 · outbound

This paper cites The Simons Observatory: Science goals and forecasts.

Emulating Recombination with Neural Networks using Universal Differential Equations The Simons Observatory: Science goals and forecasts

Reference 5

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Observation 87b532f9-5266-429d-838f-ea333f1f4d76 · outbound

This paper cites CMB-S4 Science Case, Reference Design, and Project Plan.

Emulating Recombination with Neural Networks using Universal Differential Equations CMB-S4 Science Case, Reference Design, and Project Plan

Reference 6

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Observation 8f5f3d2d-dff0-4e4d-8b25-f56f3731f8a1 · outbound

This paper cites Zeldovich, V.G.

Emulating Recombination with Neural Networks using Universal Differential Equations Zeldovich, V.G

Reference 7

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Observation 27e0222b-07ce-4e4a-ad9a-f4d2ad946657 · outbound

This paper cites Peebles, Recombination of the Primeval Plasma , ApJ 153 (1968) 1.

Emulating Recombination with Neural Networks using Universal Differential Equations Peebles, Recombination of the Primeval Plasma , ApJ 153 (1968) 1

Reference 8

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Observation 4c756ec1-0b84-4ab2-9de9-16e3f81a62bc · outbound

This paper cites Seager, D.D.

Emulating Recombination with Neural Networks using Universal Differential Equations Seager, D.D

Reference 9

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Observation 3fecff25-ef2f-4de4-8d18-00f3846ab516 · outbound

This paper cites Ali-Ha ¨ ımoud and C.M.

Emulating Recombination with Neural Networks using Universal Differential Equations Ali-Ha ¨ ımoud and C.M

Reference 10

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Observation 1e3a4d52-5cd4-4067-a72c-7479c8ed3511 · outbound

This paper cites Lee and Y.

Emulating Recombination with Neural Networks using Universal Differential Equations Lee and Y

Reference 11

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Observation 8b90ddba-79b0-4b23-a274-79677d801f0e · outbound

This paper cites Universal Differential Equations for Scientific Machine Learning.

Emulating Recombination with Neural Networks using Universal Differential Equations Universal Differential Equations for Scientific Machine Learning

Reference 12

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Observation 7c32a365-4bb3-4eca-92c4-c0e7a87b18f0 · outbound

This paper cites Bolibar, F.

Emulating Recombination with Neural Networks using Universal Differential Equations Bolibar, F

Reference 13

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Observation 86feaf92-a8b1-41b7-ba85-3609bf3f000e · outbound

This paper cites Lima, C.M.

Emulating Recombination with Neural Networks using Universal Differential Equations Lima, C.M

Reference 14

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Observation aa8a1691-45e7-40d3-99c7-5a9249e5713e · outbound

This paper cites Vortmeyer-Kley, P.

Emulating Recombination with Neural Networks using Universal Differential Equations Vortmeyer-Kley, P

Reference 15

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Observation 0913147d-b9ad-44a7-bd85-4aa481a6e9d7 · outbound

This paper cites The Coyote Universe II: Cosmological Models and Precision Emulation of the Nonlinear Matter Power Spectrum.

Emulating Recombination with Neural Networks using Universal Differential Equations The Coyote Universe II: Cosmological Models and Precision Emulation of the Nonlinear Matter Power Spectrum

Reference 16

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Observation 88e2fe03-d14b-4b89-9b8b-1c1340107772 · outbound

This paper cites {\sc CosmoNet}: fast cosmological parameter estimation in non-flat models using neural networks.

Emulating Recombination with Neural Networks using Universal Differential Equations {\sc CosmoNet}: fast cosmological parameter estimation in non-flat models using neural networks

Reference 17

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Observation bc9a68ef-ec24-4399-9caa-88955f022f4a · outbound

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Emulating Recombination with Neural Networks using Universal Differential Equations Parameter Inference for Weak Lensing using Gaussian Processes and MOPED

Reference 18

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Observation 2e982048-fd4f-4552-bcda-da33711bcc9d · outbound

This paper cites COSMOPOWER: emulating cosmological power spectra for accelerated Bayesian inference from next-generation surveys.

Emulating Recombination with Neural Networks using Universal Differential Equations COSMOPOWER: emulating cosmological power spectra for accelerated Bayesian inference from next-generation surveys

Reference 19

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Observation c05c8184-c520-4349-a621-1ffa7731c08e · outbound

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Emulating Recombination with Neural Networks using Universal Differential Equations Pico: Parameters for the Impatient Cosmologist

Reference 20

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Observation c572b197-36fd-4b0d-ba96-e269c6415344 · outbound

This paper cites Euclid preparation: IX. EuclidEmulator2 -- Power spectrum emulation with massive neutrinos and self-consistent dark energy perturbations.

Emulating Recombination with Neural Networks using Universal Differential Equations Euclid preparation: IX. EuclidEmulator2 -- Power spectrum emulation with massive neutrinos and self-consistent dark energy perturbations

Reference 21

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Emulating Recombination with Neural Networks using Universal Differential Equations Accelerating Large-Scale-Structure data analyses by emulating Boltzmann solvers and Lagrangian Perturbation Theory

Reference 22

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Emulating Recombination with Neural Networks using Universal Differential Equations The BACCO Simulation Project: Exploiting the full power of large-scale structure for cosmology

Reference 23

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Emulating Recombination with Neural Networks using Universal Differential Equations CosmicNet I: Physics-driven implementation of neural networks within Boltzmann-Einstein solvers

Reference 24

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Emulating Recombination with Neural Networks using Universal Differential Equations Field Level Neural Network Emulator for Cosmological N-body Simulations

Reference 25

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Observation 1f3428c0-f9e2-48a1-9312-19fb94fdcf66 · outbound

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Emulating Recombination with Neural Networks using Universal Differential Equations Fast emulation of two-point angular statistics for photometric galaxy surveys

Reference 26

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Observation ae3a1d2f-deed-4aa5-b38e-47df29f95e59 · outbound

This paper cites Capse.jl: efficient and auto-differentiable CMB power spectra emulation.

Emulating Recombination with Neural Networks using Universal Differential Equations Capse.jl: efficient and auto-differentiable CMB power spectra emulation

Reference 27

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Observation cecf4e26-fe0d-4e6e-b6a7-162a49272d57 · outbound

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Emulating Recombination with Neural Networks using Universal Differential Equations Galaxy Clustering in the Mira-Titan Universe I: Emulators for the redshift space galaxy correlation function and galaxy-galaxy lensing

Reference 28

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Observation f9d8eaad-247a-4a50-8180-f05dfeaa8302 · outbound

This paper cites High-accuracy emulators for observables in $\Lambda$CDM, $N_\mathrm{eff}$, $\Sigma m_\nu$, and $w$ cosmologies.

Emulating Recombination with Neural Networks using Universal Differential Equations High-accuracy emulators for observables in $\Lambda$CDM, $N_\mathrm{eff}$, $\Sigma m_\nu$, and $w$ cosmologies

Reference 29

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Emulating Recombination with Neural Networks using Universal Differential Equations The Mira-Titan Universe IV. High Precision Power Spectrum Emulation

Reference 30

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This paper cites Accelerating cosmological inference with Gaussian processes and neural networks -- an application to LSST Y1 weak lensing and galaxy clustering.

Emulating Recombination with Neural Networks using Universal Differential Equations Accelerating cosmological inference with Gaussian processes and neural networks -- an application to LSST Y1 weak lensing and galaxy clustering

Reference 31

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Emulating Recombination with Neural Networks using Universal Differential Equations COMET: Clustering Observables Modelled by Emulated perturbation Theory

Reference 32

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Emulating Recombination with Neural Networks using Universal Differential Equations CosmicNet II: Emulating extended cosmologies with efficient and accurate neural networks

Reference 33

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Observation c3e3a4f4-c84e-46fc-8c1d-993347972ab7 · outbound

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Emulating Recombination with Neural Networks using Universal Differential Equations NECOLA: Towards a Universal Field-level Cosmological Emulator

Reference 34

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Observation 0f797902-c521-4123-8780-b7f7df772b6c · outbound

This paper cites $\texttt{matryoshka}$: Halo Model Emulator for the Galaxy Power Spectrum.

Emulating Recombination with Neural Networks using Universal Differential Equations $\texttt{matryoshka}$: Halo Model Emulator for the Galaxy Power Spectrum

Reference 35

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Emulating Recombination with Neural Networks using Universal Differential Equations Kernel-Based Emulator for the 3D Matter Power Spectrum from CLASS

Reference 36

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Emulating Recombination with Neural Networks using Universal Differential Equations Multi-Fidelity Emulation for the Matter Power Spectrum using Gaussian Processes

Reference 37

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local_arxiv, observed 2026-08-12T14:31:03.900639Z

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No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-12T14:31:03.611994Z digest=sha256:2b06d4cc2e64fb7f16538c3b413a317143863ff933e09fe541b6144792f52feb

Observation d9d465ac-1241-4827-9f4c-d265f92ee067 · outbound

This paper cites The cosmology dependence of galaxy clustering and lensing from a hybrid $N$-body-perturbation theory model.

Emulating Recombination with Neural Networks using Universal Differential Equations The cosmology dependence of galaxy clustering and lensing from a hybrid $N$-body-perturbation theory model

Reference 38

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source=pdf_text observed=2026-08-12T14:31:03.614676Z digest=sha256:6c7aa177f81282be7aedd17d3f9d4ed217a4e82872e01748ed9444625ababbfd

Observation 408ac96a-79fc-472b-ac0c-b2e40cfa9841 · outbound

This paper cites HMcode-2020: Improved modelling of non-linear cosmological power spectra with baryonic feedback.

Emulating Recombination with Neural Networks using Universal Differential Equations HMcode-2020: Improved modelling of non-linear cosmological power spectra with baryonic feedback

Reference 39

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source=pdf_text observed=2026-08-12T14:31:03.618051Z digest=sha256:fb6e867c9ad5a3cb131e3a9dde077f9fd52f1c37f7e29927f0e70289dba3d618

Observation 39d8d1fa-e496-412d-9f3f-f9277941bd3b · outbound

This paper cites Accurate emulator for the redshift-space power spectrum of dark matter halos and its application to galaxy power spectrum.

Emulating Recombination with Neural Networks using Universal Differential Equations Accurate emulator for the redshift-space power spectrum of dark matter halos and its application to galaxy power spectrum

Reference 40

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source=pdf_text observed=2026-08-12T14:31:03.620829Z digest=sha256:5edecdc05bc0927b42f0495d454bf40e30654e917d6e938a2c431990a294e64a

Observation 54f7c8a8-a01e-4c06-8c3b-94ae5ccac6a4 · outbound

This paper cites Cosmology with galaxy-galaxy lensing on non-perturbative scales: Emulation method and application to BOSS LOWZ.

Emulating Recombination with Neural Networks using Universal Differential Equations Cosmology with galaxy-galaxy lensing on non-perturbative scales: Emulation method and application to BOSS LOWZ

Reference 41

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source=pdf_text observed=2026-08-12T14:31:03.623444Z digest=sha256:eba6cb08bd1842e4ef91bc6c30c3c703eb364dd0e88ddc7ebca3f9fa9df2397b

Observation 4ed77ceb-25ba-4cf9-a8a0-a52c748a2992 · outbound

This paper cites Dark Quest. I. Fast and Accurate Emulation of Halo Clustering Statistics and Its Application to Galaxy Clustering.

Emulating Recombination with Neural Networks using Universal Differential Equations Dark Quest. I. Fast and Accurate Emulation of Halo Clustering Statistics and Its Application to Galaxy Clustering

Reference 42

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source=pdf_text observed=2026-08-12T14:31:03.625999Z digest=sha256:c3c10c24fdda4b1487317852e7c18d1943c06e7e905938985b564471c981f8d8

Observation 7a5668d6-34cf-4aba-a3b5-f3b809bfeaa0 · outbound

This paper cites The Aemulus Project III: Emulation of the Galaxy Correlation Function.

Emulating Recombination with Neural Networks using Universal Differential Equations The Aemulus Project III: Emulation of the Galaxy Correlation Function

Reference 43

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source=pdf_text observed=2026-08-12T14:31:03.628750Z digest=sha256:c85736f502692020a3b9d6b6b2ffc8f0b362c48ffe4cd861e52b9e68fef0d9f6

Observation 6130cf9f-b2bb-4081-94d3-980d6e4da24b · outbound

This paper cites An Emulator for the Lyman-alpha Forest.

Emulating Recombination with Neural Networks using Universal Differential Equations An Emulator for the Lyman-alpha Forest

Reference 44

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source=pdf_text observed=2026-08-12T14:31:03.631480Z digest=sha256:c2d965478c5a321d9e651e0b17f713c76e828916fb56c170d40078b38422791b

Observation e93c87dd-ebe0-4773-9e19-7f93a61dbb16 · outbound

This paper cites Emulation of reionization simulations for Bayesian inference of astrophysics parameters using neural networks.

Emulating Recombination with Neural Networks using Universal Differential Equations Emulation of reionization simulations for Bayesian inference of astrophysics parameters using neural networks

Reference 45

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no resolver link, observed 2026-08-12T14:31:03.634276Z

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source=pdf_text observed=2026-08-12T14:31:03.634276Z digest=sha256:603fc523fe66d8d0c1e4d15e1663d1fadbdc5b8269e3d31ffdd8ed32e44d76d8

Observation 180df48f-e7ce-430e-a9a5-e01d9622409f · outbound

This paper cites The Mira-Titan Universe II: Matter Power Spectrum Emulation.

Emulating Recombination with Neural Networks using Universal Differential Equations The Mira-Titan Universe II: Matter Power Spectrum Emulation

Reference 46

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no resolver link, observed 2026-08-12T14:31:03.636969Z

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source=pdf_text observed=2026-08-12T14:31:03.636969Z digest=sha256:69e3b171f674483dfae38984784a3f9d61b3c94e657e92c2aea407a81a8fdfdd

Observation efe5da96-6679-4071-b267-32d69e687b3b · outbound

This paper cites Cosmic Emulation: Fast Predictions for the Galaxy Power Spectrum.

Emulating Recombination with Neural Networks using Universal Differential Equations Cosmic Emulation: Fast Predictions for the Galaxy Power Spectrum

Reference 47

Resolution
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source=pdf_text observed=2026-08-12T14:31:03.639570Z digest=sha256:792304dae2be852c674cbf733b16108d4d11c6b42ae4c92ac837f5cf85160e64

Observation 9f91cb4a-60d2-4e36-9a3a-ce5a7e664342 · outbound

This paper cites PkANN - II. A non-linear matter power spectrum interpolator developed using artificial neural networks.

Emulating Recombination with Neural Networks using Universal Differential Equations PkANN - II. A non-linear matter power spectrum interpolator developed using artificial neural networks

Reference 48

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source=pdf_text observed=2026-08-12T14:31:03.642550Z digest=sha256:1de8c3cb3ac9e0a4a5ee913a8537263646a3f690efe71041e0062020cfa4ca0f

Observation e8fa0be3-f529-4a99-84c1-a9f4f4aad6a8 · outbound

This paper cites The Coyote Universe Extended: Precision Emulation of the Matter Power Spectrum.

Emulating Recombination with Neural Networks using Universal Differential Equations The Coyote Universe Extended: Precision Emulation of the Matter Power Spectrum

Reference 49

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no resolver link, observed 2026-08-12T14:31:03.645172Z

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source=pdf_text observed=2026-08-12T14:31:03.645172Z digest=sha256:c1825bfe7f621fb0b6b17e4d7f513e43a21535b3a37905bc604c26142c03ebd6

Observation e363e3d4-29e0-4ff4-9655-371f6285ecb5 · outbound

This paper cites The Coyote Universe III: Simulation Suite and Precision Emulator for the Nonlinear Matter Power Spectrum.

Emulating Recombination with Neural Networks using Universal Differential Equations The Coyote Universe III: Simulation Suite and Precision Emulator for the Nonlinear Matter Power Spectrum

Reference 50

Resolution
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source=pdf_text observed=2026-08-12T14:31:03.647864Z digest=sha256:73873bb454040f40911dbdcecd70bd0dd59ad1483494d0bf47e1020188fa339a

Observation 1fe14749-1da1-41cc-b33d-a6927aa8e657 · outbound

This paper cites H0 tension or T0 tension?.

Emulating Recombination with Neural Networks using Universal Differential Equations H0 tension or T0 tension?

Reference 51

Resolution
verified exact
local_arxiv, observed 2026-08-12T14:31:03.815338Z

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No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-12T14:31:03.650658Z digest=sha256:1c933ae575760862b5dfeec9d299af88c4afcad41e3cc900a1ce2a3d9177e47a

Observation 6eeb1a84-af53-4d48-b8af-8aefe0055804 · outbound

This paper cites Tsitouras, Runge–kutta pairs of order 5(4) satisfying only the first column simplifying assumption, Computers & Mathematics with Applications 62 (2011) 770.

Emulating Recombination with Neural Networks using Universal Differential Equations Tsitouras, Runge–kutta pairs of order 5(4) satisfying only the first column simplifying assumption, Computers & Mathematics with Applications 62 (2011) 770

Reference 52

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raw_fallback, observed 2026-08-12T14:31:04.092094Z

Source-reported events for the cited work

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source=pdf_text observed=2026-08-12T14:31:03.653207Z digest=sha256:76753e9fa406906a36207efbb77f1b0336259419baba3196a0c58487fd19ee5f

Observation 8a7c9ede-ea7a-4e40-8a54-ee6623fa7c61 · outbound

This paper cites Kingma and J.

Emulating Recombination with Neural Networks using Universal Differential Equations Kingma and J

Reference 53

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source=pdf_text observed=2026-08-12T14:31:03.655322Z digest=sha256:6522423c603757662f85d99954b9684082e55de8766bd8db0adbe4ddb0c6e9a1

Observation 734dccf3-c7b3-41d8-929e-caa10f772fe7 · outbound

This paper cites DiffEqFlux.jl - A Julia Library for Neural Differential Equations.

Emulating Recombination with Neural Networks using Universal Differential Equations DiffEqFlux.jl - A Julia Library for Neural Differential Equations

Reference 54

Resolution
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source=pdf_text observed=2026-08-12T14:31:03.657360Z digest=sha256:1de232019a01a205cb917143328b10c5805d287cc26162d53469d160216dd3ef

Observation 668e6482-eefe-47c7-b590-ea9ec63eeb35 · outbound

This paper cites Multiple shooting for training neural differential equations on time series.

Emulating Recombination with Neural Networks using Universal Differential Equations Multiple shooting for training neural differential equations on time series

Reference 55

Resolution
verified exact
local_arxiv, observed 2026-08-12T14:31:03.799093Z

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No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-12T14:31:03.660615Z digest=sha256:c0cd793836bbd790e2c39b047b8ec3e68adb1ef634c8661d901e125aaebe2423

Observation 4c1a8125-5aca-442e-b31b-496f7bc52978 · outbound

This paper cites Seager, D.D.

Emulating Recombination with Neural Networks using Universal Differential Equations Seager, D.D

Reference 56

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T14:31:04.078304Z

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No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-12T14:31:03.662851Z digest=sha256:be2add96b6b4cb85df9d22479ada52f3c65f07c1f6b3fda5167a4dde6fd2ea81

Observation 1ac98368-8239-4dfb-acfe-f9c4adc47f70 · outbound

This paper cites Hazumi, P.A.R.

Emulating Recombination with Neural Networks using Universal Differential Equations Hazumi, P.A.R

Reference 57

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