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Paper Citation Record · LEDGER

Fast Amortized Fitting of Scientific Signals Across Time and Ensembles via Transferable Neural Fields

As of 10 August 2026, this Paper Citation Record lists 26 of 26 outbound references and 0 inbound Pith citation observations for arXiv:2604.19979.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2604.19979 v1

Coverage vector

measured 26 of 26 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-05-10T02:44:54.555641Z

measured 26 of 26 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: cited_works

Reference resolution

26 of 26 outbound references displayed

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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 96415cc0-8752-4358-99ab-2bc9df0e1400 · outbound

This paper cites Visualization for scientific discovery, decision-making, and communica- tion.

Fast Amortized Fitting of Scientific Signals Across Time and Ensembles via Transferable Neural Fields Visualization for scientific discovery, decision-making, and communica- tion

Reference 1

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Observation 8d644cb7-7dae-4b66-9f1c-061dfbb5fd9c · outbound

This paper cites The catalogue for astrophysical turbulence sim- ulations (cats).The Astrophysical Journal, 905(1):14.

Fast Amortized Fitting of Scientific Signals Across Time and Ensembles via Transferable Neural Fields The catalogue for astrophysical turbulence sim- ulations (cats).The Astrophysical Journal, 905(1):14

Reference 2

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Observation f64f3a38-6ddb-4ca0-98e6-479b53ef8295 · outbound

This paper cites Reynolds number effects on rayleigh–taylor instability with possible implica- tions for type ia supernovae.Nature Physics, 2(8):562–568.

Fast Amortized Fitting of Scientific Signals Across Time and Ensembles via Transferable Neural Fields Reynolds number effects on rayleigh–taylor instability with possible implica- tions for type ia supernovae.Nature Physics, 2(8):562–568

Reference 3

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Observation 6bed3999-f2ab-4ebd-bbeb-03b89185da88 · outbound

This paper cites Transformers as meta- learners for implicit neural representations.

Fast Amortized Fitting of Scientific Signals Across Time and Ensembles via Transferable Neural Fields Transformers as meta- learners for implicit neural representations

Reference 4

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Source-reported events for the cited work

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Observation 645bd073-b69c-4766-b287-ad461f0d7143 · outbound

This paper cites Coin: Compression with implicit neural representations.

Fast Amortized Fitting of Scientific Signals Across Time and Ensembles via Transferable Neural Fields Coin: Compression with implicit neural representations

Reference 5

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Source-reported events for the cited work

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Observation 4396d73f-3737-4573-a083-5bd75f83f5e4 · outbound

This paper cites K-planes: Explicit radiance fields in space, time, and appearance.

Fast Amortized Fitting of Scientific Signals Across Time and Ensembles via Transferable Neural Fields K-planes: Explicit radiance fields in space, time, and appearance

Reference 6

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Observation 50583e35-997c-4f54-9923-e58c44bd32fc · outbound

This paper cites Gisler, Tamra Heberling, Catherine S.

Fast Amortized Fitting of Scientific Signals Across Time and Ensembles via Transferable Neural Fields Gisler, Tamra Heberling, Catherine S

Reference 7

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Source-reported events for the cited work

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Observation 5816be2f-3c42-484b-a670-179e3dfeba37 · outbound

This paper cites an unresolved cited work.

Fast Amortized Fitting of Scientific Signals Across Time and Ensembles via Transferable Neural Fields Unresolved cited work

Reference 8

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Observation 7e20ccd1-2ce1-46f8-bc6b-eee93b0d2584 · outbound

This paper cites Surrogate Modeling for Computationally Expensive Simulations of Supernovae in High-Resolution Galaxy Simulations.

Fast Amortized Fitting of Scientific Signals Across Time and Ensembles via Transferable Neural Fields Surrogate Modeling for Computationally Expensive Simulations of Supernovae in High-Resolution Galaxy Simulations

Reference 9

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Observation b405436b-d7cc-4363-a916-c640d772c987 · outbound

This paper cites Nirvana: Neural implicit representations of videos with adaptive networks and autore- gressive patch-wise modeling.

Fast Amortized Fitting of Scientific Signals Across Time and Ensembles via Transferable Neural Fields Nirvana: Neural implicit representations of videos with adaptive networks and autore- gressive patch-wise modeling

Reference 10

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Observation eb91eb45-58e9-463a-a2b7-e621bed6a512 · outbound

This paper cites Instant neural graphics primitives with a mul- tiresolution hash encoding.ACM transactions on graphics (TOG), 41(4):1–15.

Fast Amortized Fitting of Scientific Signals Across Time and Ensembles via Transferable Neural Fields Instant neural graphics primitives with a mul- tiresolution hash encoding.ACM transactions on graphics (TOG), 41(4):1–15

Reference 11

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Source-reported events for the cited work

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Observation 2929490f-1fe4-4567-a071-c0485a514d85 · outbound

This paper cites Understanding sinusoidal neural networks.

Fast Amortized Fitting of Scientific Signals Across Time and Ensembles via Transferable Neural Fields Understanding sinusoidal neural networks

Reference 12

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Observation bd24d963-204b-4800-91cb-63a189c304b7 · outbound

This paper cites The well: a large-scale collection of diverse physics simulations for machine learning.Advances in Neural Information Pro- cessing Systems, 37:44989–45037.

Fast Amortized Fitting of Scientific Signals Across Time and Ensembles via Transferable Neural Fields The well: a large-scale collection of diverse physics simulations for machine learning.Advances in Neural Information Pro- cessing Systems, 37:44989–45037

Reference 13

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Observation 1fd3e84b-3757-45bb-b7ad-cb17ffd7aabc · outbound

This paper cites Wire: Wavelet implicit neural representations.

Fast Amortized Fitting of Scientific Signals Across Time and Ensembles via Transferable Neural Fields Wire: Wavelet implicit neural representations

Reference 14

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Observation c2f1a2a2-45d0-4ceb-9789-00acf5014c4c · outbound

This paper cites Implicit neural representa- tions with periodic activation functions.NeurIPS.

Fast Amortized Fitting of Scientific Signals Across Time and Ensembles via Transferable Neural Fields Implicit neural representa- tions with periodic activation functions.NeurIPS

Reference 15

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Observation 41072309-26af-4386-8beb-76f6d279f8be · outbound

This paper cites Piner: Prior- informed implicit neural representation learning for test-time adaptation in sparse-view ct reconstruction.

Fast Amortized Fitting of Scientific Signals Across Time and Ensembles via Transferable Neural Fields Piner: Prior- informed implicit neural representation learning for test-time adaptation in sparse-view ct reconstruction

Reference 16

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Observation b7c38fe8-d526-42af-9e40-b3b405d028b0 · outbound

This paper cites Adaptive multi-resolution encoding for interactive large- scale volume visualization through functional approxima- tion.

Fast Amortized Fitting of Scientific Signals Across Time and Ensembles via Transferable Neural Fields Adaptive multi-resolution encoding for interactive large- scale volume visualization through functional approxima- tion

Reference 17

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Observation 92329dd6-2c21-4a16-bd1a-2cc55baa173f · outbound

This paper cites F-hash: Feature-based hash design for time-varying vol- ume visualization via multi-resolution tesseract encoding.

Fast Amortized Fitting of Scientific Signals Across Time and Ensembles via Transferable Neural Fields F-hash: Feature-based hash design for time-varying vol- ume visualization via multi-resolution tesseract encoding

Reference 18

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Observation b72cc43c-5f0f-4dda-bc31-50551147e62f · outbound

This paper cites Srinivasan, Jonathan T.

Fast Amortized Fitting of Scientific Signals Across Time and Ensembles via Transferable Neural Fields Srinivasan, Jonathan T

Reference 19

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Observation 4823053c-2681-4a4c-b640-971430c20130 · outbound

This paper cites Learning transferable features for implicit neural representations.

Fast Amortized Fitting of Scientific Signals Across Time and Ensembles via Transferable Neural Fields Learning transferable features for implicit neural representations

Reference 20

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Observation 90554540-7c8c-4584-b871-fe0fbe8dcf71 · outbound

This paper cites Fit pixels, get labels: Meta-learned implicit networks for image segmentation.

Fast Amortized Fitting of Scientific Signals Across Time and Ensembles via Transferable Neural Fields Fit pixels, get labels: Meta-learned implicit networks for image segmentation

Reference 21

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This paper cites Doyle, and Kwan-Liu Ma.

Fast Amortized Fitting of Scientific Signals Across Time and Ensembles via Transferable Neural Fields Doyle, and Kwan-Liu Ma

Reference 22

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Observation ba552e75-036f-4cb6-bc8e-a50c0dcea265 · outbound

This paper cites Adaptively placed multi-grid scene representation networks for large-scale data visualization.

Fast Amortized Fitting of Scientific Signals Across Time and Ensembles via Transferable Neural Fields Adaptively placed multi-grid scene representation networks for large-scale data visualization

Reference 23

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Source-reported events for the cited work

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Observation 6afb3bc4-4bab-4679-b3da-ec33afaf1dc5 · outbound

This paper cites Diner: Disorder-invariant implicit neural representation.

Fast Amortized Fitting of Scientific Signals Across Time and Ensembles via Transferable Neural Fields Diner: Disorder-invariant implicit neural representation

Reference 24

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Source-reported events for the cited work

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Observation ab03d586-22d9-4972-b601-29a41d44d3c5 · outbound

This paper cites Model Considerations In addition toSIREN[15] andK-Planes[6], we use a modifiedfhashencoder [18] as our spatial encoding-based model.

Fast Amortized Fitting of Scientific Signals Across Time and Ensembles via Transferable Neural Fields Model Considerations In addition toSIREN[15] andK-Planes[6], we use a modifiedfhashencoder [18] as our spatial encoding-based model

Reference 25

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Observation 98a44413-79aa-47f3-889a-d60f2b281a83 · outbound

This paper cites Each transfor- mation produces a sequence of signals with progressively increasing variation over time while preserving underly- ing structure.

Fast Amortized Fitting of Scientific Signals Across Time and Ensembles via Transferable Neural Fields Each transfor- mation produces a sequence of signals with progressively increasing variation over time while preserving underly- ing structure

Reference 26

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verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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