Pith. sign in

Paper Citation Record · LEDGER

Physics-Informed Deep Inverse Operator Networks for Solving PDE Inverse Problems

As of 14 August 2026, this Paper Citation Record lists 21 of 21 outbound references and 4 inbound Pith citation observations for arXiv:2412.03161.

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

pith.paper-citation-record.v1
2412.03161 v2

Coverage vector

measured 21 of 21 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-11T22:48:13.424353Z

measured 25 of 25 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-13T06:32:02.005865+00:00

measured 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-10T20:13:16.611531Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-19T18:57:43.148432Z

Reference resolution

21 of 21 outbound references displayed

  • verified exact0
  • verified fuzzy9
  • unresolved11
  • parse uncertain0
  • malformed identifier1
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 02bd4192-1f2a-465a-bd86-c1452c5a7e93 · outbound

This paper cites an unresolved cited work.

Physics-Informed Deep Inverse Operator Networks for Solving PDE Inverse Problems Unresolved cited work

Reference 1

Resolution
malformed identifier
raw_fallback, observed 2026-08-11T22:48:14.914845Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-08-11T22:48:13.144641Z digest=sha256:524ba55a775818055b8705edcdab18d84f1d67e908668942e71cac967302b4d2

Observation 51ea12c6-13c3-4b16-83ec-b9e40797899c · outbound

This paper cites an unresolved cited work.

Physics-Informed Deep Inverse Operator Networks for Solving PDE Inverse Problems Unresolved cited work

Reference 2

Resolution
unresolved
raw_fallback, observed 2026-08-11T22:48:14.451063Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-08-11T22:48:13.254763Z digest=sha256:80831ae59e71e67831bcfd40a9347dae2339013a49a9d8e3a690e20d589da4d9

Observation 8ad1b7c1-4e12-40da-9f9e-298099b2f34e · outbound

This paper cites an unresolved cited work.

Physics-Informed Deep Inverse Operator Networks for Solving PDE Inverse Problems Unresolved cited work

Reference 3

Resolution
unresolved
raw_fallback, observed 2026-08-11T22:48:15.204755Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-08-11T22:48:13.128279Z digest=sha256:07796b7517ebd46df579298ae3874777a7d8681562a7b5ea1765264f9ed2c2ab

Observation 15cc45c3-536d-4e71-83f9-ac0e18c4e2f0 · outbound

This paper cites P {xk}K k=1 ∈ Ω, {xj}M j=1 ∈ ∂Ω, {u(i)}N i=1 ∈ U sup η,θ | eLphysics(η, θ) − Lphysics(η, θ,{xk}K k=1, {xj}M j=1)| > 4α ! ≤ 8Ncexp − α2N K 32|Ω|2R4 N + 8Ncexp − α2N M 32|∂Ω|2R4 B.

Physics-Informed Deep Inverse Operator Networks for Solving PDE Inverse Problems P {xk}K k=1 ∈ Ω, {xj}M j=1 ∈ ∂Ω, {u(i)}N i=1 ∈ U sup η,θ | eLphysics(η, θ) − Lphysics(η, θ,{xk}K k=1, {xj}M j=1)| > 4α ! ≤ 8Ncexp − α2N K 32|Ω|2R4 N + 8Ncexp − α2N M 32|∂Ω|2R4 B

Reference 4

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T22:48:14.104755Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-08-11T22:48:13.344740Z digest=sha256:e7e889660f7172322c6dc329524716e6bdc1426424b285dd22dc66f4f5470828

Observation 8de005e9-ea48-4bfb-ac39-3b76c0fc2e2e · outbound

This paper cites Variable-Input Deep Operator Networks.

Physics-Informed Deep Inverse Operator Networks for Solving PDE Inverse Problems Variable-Input Deep Operator Networks

Reference 5

Resolution
unresolved
no resolver link, observed 2026-08-11T22:48:13.044664Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T22:48:13.044664Z digest=sha256:a8050affc78a1d3fa10b0513e610ab7f25c128cdf24045e08d19974246ccac61

Observation e5ab7d9d-ad35-4881-a164-ebd6d7df4057 · outbound

This paper cites The final embedding is obtained by computing the inner product of their outputs.

Physics-Informed Deep Inverse Operator Networks for Solving PDE Inverse Problems The final embedding is obtained by computing the inner product of their outputs

Reference 6

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T22:48:14.714756Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-08-11T22:48:13.165719Z digest=sha256:8cf5dfb2abaebb85a52c11d4bca7d1d60f9b6d89a44f4a373fe6cc64e20bf6d1

Observation c00aaa94-1692-4973-ac6c-b7ec65169268 · outbound

This paper cites Latent Neural Operator for Solving Forward and Inverse PDE Problems.

Physics-Informed Deep Inverse Operator Networks for Solving PDE Inverse Problems Latent Neural Operator for Solving Forward and Inverse PDE Problems

Reference 8

Resolution
unresolved
no resolver link, observed 2026-08-11T22:48:13.107656Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T22:48:13.107656Z digest=sha256:947fa50a0fa756b0374b8687a3154752fa3d5e3041c82322ec878b4961b85494

Observation d6a96373-8245-4b85-8046-c04b4a9b47e0 · outbound

This paper cites The inverse branch network takes partial measurement data as input and produces the coefficient vector for the target function.

Physics-Informed Deep Inverse Operator Networks for Solving PDE Inverse Problems The inverse branch network takes partial measurement data as input and produces the coefficient vector for the target function

Reference 9

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T22:48:15.334847Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-08-11T22:48:13.117580Z digest=sha256:b66f3952c9d70941e42a1a0e8eb4d49b3425fe367c81b2249ae1c6e58fb2c852

Observation b0e9e663-3976-44e1-832a-abe19f2e46db · outbound

This paper cites an unresolved cited work.

Physics-Informed Deep Inverse Operator Networks for Solving PDE Inverse Problems Unresolved cited work

Reference 15

Resolution
unresolved
raw_fallback, observed 2026-08-11T22:48:14.562888Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-08-11T22:48:13.214743Z digest=sha256:739f082a36c6a58da60a3ecd373bdd703bf887d498d16e88abe544dc0e277934

Observation fd6de86f-1c4a-429f-a621-d2879d5f8542 · outbound

This paper cites an unresolved cited work.

Physics-Informed Deep Inverse Operator Networks for Solving PDE Inverse Problems Unresolved cited work

Reference 17

Resolution
unresolved
raw_fallback, observed 2026-08-11T22:48:14.294792Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-08-11T22:48:13.293225Z digest=sha256:368208b1f7a05e8ec8b3535fa52444f8d72a25b24aa29d4ab03fb62acce83731

Observation 871b6bce-a033-4470-b1c7-7e87bafdfecc · outbound

This paper cites P {xl}L l=1 ∈ Ωm, {u(i)}N i=1 ∈ UN sup η,θ | eLdata(η, θ) − Ldata(η, θ,{xl}L l=1)| > 2α ! ≤ 8Ncexp − α2N L 512|Ωm|2R4 Proof.

Physics-Informed Deep Inverse Operator Networks for Solving PDE Inverse Problems P {xl}L l=1 ∈ Ωm, {u(i)}N i=1 ∈ UN sup η,θ | eLdata(η, θ) − Ldata(η, θ,{xl}L l=1)| > 2α ! ≤ 8Ncexp − α2N L 512|Ωm|2R4 Proof

Reference 18

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T22:48:14.184908Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-08-11T22:48:13.308947Z digest=sha256:c59782bbc5214ca60ad00e1c5813a0a4b2e4a1e1016861bf239ef426defddcfd

Observation b7c6b14e-7a60-4081-a752-d3e6772f1d4b · outbound

This paper cites an unresolved cited work.

Physics-Informed Deep Inverse Operator Networks for Solving PDE Inverse Problems Unresolved cited work

Reference 20

Resolution
unresolved
raw_fallback, observed 2026-08-11T22:48:13.995217Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-08-11T22:48:13.385144Z digest=sha256:0674bcd651a9288d28460423f7c0e484fb97f44f9bd8686545297f1e7722d436

Observation 8b963157-3ab5-4e1c-9c2e-f01beb8c4d6f · outbound

This paper cites Assume that the branch network and trunk network in PI-DIONs have continuous, non-polynomial activation functions.

Physics-Informed Deep Inverse Operator Networks for Solving PDE Inverse Problems Assume that the branch network and trunk network in PI-DIONs have continuous, non-polynomial activation functions

Reference 21

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T22:48:13.944757Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-08-11T22:48:13.424353Z digest=sha256:fc9a0377ad0bb49de17d386f2ac623aed8b8a0105a1362895861d63331e85ad8

Observation 5c324959-395d-4c4a-ae81-d123a8cb679c · outbound

This paper cites an unresolved cited work.

Physics-Informed Deep Inverse Operator Networks for Solving PDE Inverse Problems Unresolved cited work

Reference 900

Resolution
unresolved
raw_fallback, observed 2026-08-11T22:48:15.075075Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-08-11T22:48:13.140195Z digest=sha256:677df3cacbee0cf26d8f9a5da30517813a52fd5f4893ef2e9dff392953161aa1

Observation 245cc065-2532-4b2b-aed4-492993b55d20 · outbound

This paper cites N 100 500 1000 2000 Relative L2 error 28.73% 5.07% 1.03% 0.98% C.3 V ARIABLE -INPUT PI-DION S Figure 6: Schematic illustration of variable-input PI-DIONs architecture.

Physics-Informed Deep Inverse Operator Networks for Solving PDE Inverse Problems N 100 500 1000 2000 Relative L2 error 28.73% 5.07% 1.03% 0.98% C.3 V ARIABLE -INPUT PI-DION S Figure 6: Schematic illustration of variable-input PI-DIONs architecture

Reference 1000

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T22:48:14.774784Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-08-11T22:48:13.154754Z digest=sha256:d940baf2110da439a04f1a2f47376aec356f18e60dfaa1d85097d9a14aeb1dbe

Observation f7adb587-75bc-4bbd-806a-210563e39495 · outbound

This paper cites Learning time-dependent PDE via graph neural networks and deep operator network for robust accuracy on irregular grids.

Physics-Informed Deep Inverse Operator Networks for Solving PDE Inverse Problems Learning time-dependent PDE via graph neural networks and deep operator network for robust accuracy on irregular grids

Reference 1995

Resolution
unresolved
no resolver link, observed 2026-08-11T22:48:12.884602Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T22:48:12.884602Z digest=sha256:89f3e51031eeaac0d995b3760c488c85722f96356829fac69718172174ca6342

Observation eee76db0-519e-4cee-bb4f-ef7151f2aff5 · outbound

This paper cites A pinn approach for identifying governing parameters of noisy thermoacoustic systems.

Physics-Informed Deep Inverse Operator Networks for Solving PDE Inverse Problems A pinn approach for identifying governing parameters of noisy thermoacoustic systems

Reference 2018

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T22:48:15.421903Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-08-11T22:48:13.102172Z digest=sha256:ee31d166349ff0f0be0b33260803d102f45087a94ac977d918e317753a3840c6

Observation 48310f07-46d8-46f7-9c2e-417f0c3f9198 · outbound

This paper cites Convolutional neural operators.

Physics-Informed Deep Inverse Operator Networks for Solving PDE Inverse Problems Convolutional neural operators

Reference 2019

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T22:48:15.514890Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-08-11T22:48:13.084799Z digest=sha256:cc495f0916e46a3f9c3daed398d40d1ee682f83b1baaf7b9909fe8b9722be619

Observation 3cc1ea58-5454-45f2-914b-44a65e0f3338 · outbound

This paper cites Neural Inverse Operators for Solving PDE Inverse Problems.

Physics-Informed Deep Inverse Operator Networks for Solving PDE Inverse Problems Neural Inverse Operators for Solving PDE Inverse Problems

Reference 2020

Resolution
unresolved
no resolver link, observed 2026-08-11T22:48:13.011120Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T22:48:13.011120Z digest=sha256:1bfa042ba05f212d67a17f042f41c287ac173896700be287cf2e4662e2edcca7

Observation b1023262-8899-4a99-8889-48f88f206080 · outbound

This paper cites Deep learning.

Physics-Informed Deep Inverse Operator Networks for Solving PDE Inverse Problems Deep learning

Reference 2021

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T22:48:15.620148Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-08-11T22:48:12.914755Z digest=sha256:2258efefa502cf9f2fdb9be4e9da8a5d00435ef6760ca0eeefe04f90801d067b

Observation cb482939-204e-4c8a-8734-cb106a6438fd · outbound

This paper cites Fourier Neural Operator for Parametric Partial Differential Equations.

Physics-Informed Deep Inverse Operator Networks for Solving PDE Inverse Problems Fourier Neural Operator for Parametric Partial Differential Equations

Reference 2023

Resolution
unresolved
no resolver link, observed 2026-08-11T22:48:12.964757Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T22:48:12.964757Z digest=sha256:4397931ea842f4277e75cac0615c8354ad9e2511cea8bd284e1663247245eae5

Pith citing papers

Observation 42f88179-814b-4ae4-abe2-ba35198223c6 · inbound

ELM-DeepONets: Backpropagation-Free Training of Deep Operator Networks via Extreme Learning Machines cites this paper.

ELM-DeepONets: Backpropagation-Free Training of Deep Operator Networks via Extreme Learning Machines Physics-Informed Deep Inverse Operator Networks for Solving PDE Inverse Problems

Reference 14

Resolution
unresolved
no resolver link, observed 2026-08-10T20:13:16.611531Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T20:13:16.611531Z digest=sha256:36eb0b80f6453d01a5dceae5c42e234a1adb6e09835b10d6b006c653dd89cf4a

Observation 9697a177-ab4b-40e9-b150-349e0ea450c4 · inbound

Neural Interpretable PDEs: Harmonizing Fourier Insights with Attention for Scalable and Interpretable Physics Discovery cites this paper.

Neural Interpretable PDEs: Harmonizing Fourier Insights with Attention for Scalable and Interpretable Physics Discovery Physics-Informed Deep Inverse Operator Networks for Solving PDE Inverse Problems

Reference 2023

Resolution
unresolved
no resolver link, observed 2026-08-07T13:02:02.161582Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T13:02:02.161582Z digest=sha256:dae29f20f0114e278403530b8287b4ac45b53c45ff8746eac2f6f03b2595d2d8

Observation 458e7d22-71a6-445f-85fe-ed8631678d8b · inbound

DiLO: Decoupling Generative Priors and Neural Operators via Diffusion Latent Optimization for Inverse Problems cites this paper.

DiLO: Decoupling Generative Priors and Neural Operators via Diffusion Latent Optimization for Inverse Problems Physics-Informed Deep Inverse Operator Networks for Solving PDE Inverse Problems

Reference 25

Resolution
verified exact
arxiv_id, observed 2026-05-11T09:31:05.690666Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-05-10T15:58:12.225880Z digest=sha256:e0b6414cc21189b4bf9d84c904d0d453b0d16e7a9975167a27c0df7d85a15c44

Observation 5e7a00c9-7ea5-46c7-8b06-37af7da92559 · inbound

Spatiotemporal decoupled physics-informed Stone-Weierstrass neural operator for long-time prediction of time-dependent parametric PDEs cites this paper.

Spatiotemporal decoupled physics-informed Stone-Weierstrass neural operator for long-time prediction of time-dependent parametric PDEs Physics-Informed Deep Inverse Operator Networks for Solving PDE Inverse Problems

Reference 22

Resolution
verified exact
arxiv_id, observed 2026-05-19T18:57:43.150705Z

Source-reported events for the cited work

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

source=pdf_text observed=2026-05-19T18:55:55.093897Z digest=sha256:dc6bd58568a5e4e5ccffdb5cd3f1abcac1d4da206273e513ebe2e2828905229d