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Paper Citation Record · LEDGER

Emulating Recombination with Neural Networks using Universal Differential Equations

As of 14 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

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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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Observation a90b01b4-5386-4c57-b9e9-d8cffa7bbcbf · outbound

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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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Observation b666a72f-0dba-489e-94e2-54aff64dd86f · outbound

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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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Observation 8a7e0d5f-05f5-49e2-87c0-0c8a2a42e110 · outbound

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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

This paper cites Fast emulation of two-point angular statistics for photometric galaxy surveys.

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

This paper cites Galaxy Clustering in the Mira-Titan Universe I: Emulators for the redshift space galaxy correlation function and galaxy-galaxy lensing.

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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Observation 84180ed3-8505-4164-95c3-269eb1051ee1 · outbound

This paper cites Kernel-Based Emulator for the 3D Matter Power Spectrum from CLASS.

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 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-12T14:31:03.611994Z digest=sha256:0dbbb05004097174418b3fcc473c5b5578aaf96ad84689d502a8d2340aa25388

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:c4123472540b1a34fcb73ad9b4c6340421072a11548e3d90207d62ae781b66b4

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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no resolver link, observed 2026-08-12T14:31:03.618051Z

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source=pdf_text observed=2026-08-12T14:31:03.618051Z digest=sha256:758864e4195ceb1ebc272e02673a2f6cac005ace48398043e65f45c16f04a79c

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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no resolver link, observed 2026-08-12T14:31:03.620829Z

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source=pdf_text observed=2026-08-12T14:31:03.620829Z digest=sha256:625fb1670d49486436e75fc4762b66278147f5e8c0c2c87fb2e0acaa302d514f

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=sha256:9a68a365c1ec5419b4193c3cc846f4d503c24ed404f22d7aea426c2af9bee510

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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no resolver link, observed 2026-08-12T14:31:03.625999Z

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source=pdf_text observed=2026-08-12T14:31:03.625999Z digest=sha256:7de8698a301eee8cb60de6703a3adace6ba5cd0496789580431103e560acb937

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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no resolver link, observed 2026-08-12T14:31:03.628750Z

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source=pdf_text observed=2026-08-12T14:31:03.628750Z digest=sha256:3749222ad8f5fd40c60e5bd156ab9bea4e7fe1010b3d683b8034ac8cddc57d40

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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no resolver link, observed 2026-08-12T14:31:03.631480Z

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source=pdf_text observed=2026-08-12T14:31:03.631480Z digest=sha256:bc01c95de52462f2721297dbc112abd8aea27fe324b3281f0aee1292421b63e5

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:cdc85c1a43f06ef6bd1691b956ac52d34e53c41093489644dadfd01069a730f2

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=sha256:95d934429f2e9dbb109916ba13cd5e0ccab04cd50246d67bb8f9c9bdd218b9ee

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

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no resolver link, observed 2026-08-12T14:31:03.639570Z

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source=pdf_text observed=2026-08-12T14:31:03.639570Z digest=sha256:5d3e3c091b6b27f9718cb5c12fb3efd49bca265181aac5235b8159cd5ebaba8b

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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no resolver link, observed 2026-08-12T14:31:03.642550Z

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source=pdf_text observed=2026-08-12T14:31:03.642550Z digest=sha256:0ed9f5b71c2ffafb6b0394c9895fe27f71734c364eaa7c96ff846aa603ae72aa

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:aa2d635a254c2102d6ea4732b2c847987483ab1946bc75c8a4591d38ec96ab67

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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no resolver link, observed 2026-08-12T14:31:03.647864Z

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source=pdf_text observed=2026-08-12T14:31:03.647864Z digest=sha256:9631980382e1d7875e1ba034ad71cd98f778f37c67ed41c5a15c858c511a1ede

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

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-12T14:31:03.650658Z digest=sha256:4e267bcec6d4aedf6fedf3b954a10a261149d3306930241245b6122c595f5c98

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

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-12T14:31:03.653207Z digest=sha256:7727287bc70c16ff9ee3554c5f4bfb2731844f796c2e8c658e2090c1896ceedc

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=sha256:5df4f09f91c302f5cdc56c2070e088f23f0eee2819a55444292cc19513b211b3

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:cfc6cb98a7a1f328106b9c108a4dcad9a15c2ae3d249493b05cda5075323e2a0

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

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-12T14:31:03.660615Z digest=sha256:e2b229e9f6b94faf28379880e91530bc3ef651c2906e9e67cac0f3cd8995858e

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

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-12T14:31:03.662851Z digest=sha256:e13858d58b9b8927d6850338e3c92ae6acdd9b941058cb5eea6c5cb7c64620e0

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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unresolved
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source=pdf_text observed=2026-08-12T14:31:03.665226Z digest=sha256:94aea9d91c4255b966f9890f94a53a81694fcc6df3920346c89f9e31bd81acdc

Pith citing papers

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