Paper Citation Record · LEDGER

Introduction to optimization methods for training SciML models

As of 17 August 2026, this Paper Citation Record lists 14 of 14 outbound references and 1 inbound Pith citation observation for arXiv:2601.10222.

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

pith.paper-citation-record.v1
2601.10222 v2

Coverage vector

measured 14 of 14 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-03T10:27:48.555525Z

measured 15 of 15 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-17T06:30:58.91139+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-10T18:56:16.182901Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-10T23:40:53.086156Z

Reference resolution

14 of 14 outbound references displayed

verified exact0
verified fuzzy0
unresolved14
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation f32d3552-3b27-4cb8-b4de-b3a000e80cf2 · outbound

This paper cites Ahamed, N.

Introduction to optimization methods for training SciML models Ahamed, N

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-03T10:27:47.459165Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 379cad92-07eb-42e4-9a24-8becf3e459fd · outbound

This paper cites an unresolved cited work.

Introduction to optimization methods for training SciML models Unresolved cited work

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-03T10:27:48.025576Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 10c04fe9-335c-4939-86dc-3f3ff90b22be · outbound

This paper cites an unresolved cited work.

Introduction to optimization methods for training SciML models Unresolved cited work

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-03T10:27:48.234471Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e9d9cf16-9927-4b34-96a6-96f26456b3a1 · outbound

This paper cites an unresolved cited work.

Introduction to optimization methods for training SciML models Unresolved cited work

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-03T10:27:47.859351Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5732e4e7-4836-4b8b-bafe-9ce8c72c730a · outbound

This paper cites an unresolved cited work.

Introduction to optimization methods for training SciML models Unresolved cited work

Reference 62

Resolution: unresolved; no resolver link, observed 2026-08-03T10:27:48.298988Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 02b8b6f0-d3bf-4dd7-a0ba-ca640d819341 · outbound

This paper cites SOAP: Improving and Stabilizing Shampoo using Adam.

Introduction to optimization methods for training SciML models SOAP: Improving and Stabilizing Shampoo using Adam

Reference 1998

Resolution: unresolved; no resolver link, observed 2026-08-03T10:27:48.555525Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1eb619d4-b094-4fec-9401-6c2a9ddc675a · outbound

This paper cites Combining physics-based and data-driven models: advancing the frontiers of research with Scientific Machine Learning.

Introduction to optimization methods for training SciML models Combining physics-based and data-driven models: advancing the frontiers of research with Scientific Machine Learning

Reference 2006

Resolution: unresolved; no resolver link, observed 2026-08-03T10:27:48.355455Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fda221b4-a947-4519-960e-e3a6a68ad328 · outbound

This paper cites an unresolved cited work.

Introduction to optimization methods for training SciML models Unresolved cited work

Reference 2012

Resolution: unresolved; no resolver link, observed 2026-08-03T10:27:47.741150Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 949b8118-e31f-4a1a-ad8f-8c3a32689ab8 · outbound

This paper cites an unresolved cited work.

Introduction to optimization methods for training SciML models Unresolved cited work

Reference 2014

Resolution: unresolved; no resolver link, observed 2026-08-03T10:27:48.439563Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e5b17f6e-26ca-475b-9019-d9c11cda9f26 · outbound

This paper cites Kiyani, K.

Introduction to optimization methods for training SciML models Kiyani, K

Reference 2017

Resolution: unresolved; no resolver link, observed 2026-08-03T10:27:47.957535Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6931992d-bcfb-4ab2-adbc-45a421ceca26 · outbound

This paper cites Sobolev Training for Physics Informed Neural Networks.

Introduction to optimization methods for training SciML models Sobolev Training for Physics Informed Neural Networks

Reference 2018

Resolution: unresolved; no resolver link, observed 2026-08-03T10:27:48.487293Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 50231933-6090-46fc-a852-ee9f5de450a5 · outbound

This paper cites an unresolved cited work.

Introduction to optimization methods for training SciML models Unresolved cited work

Reference 2019

Resolution: unresolved; no resolver link, observed 2026-08-03T10:27:48.158107Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 66b29a9f-f37e-48db-803f-1a75f369b3f7 · outbound

This paper cites Handbook of Convergence Theorems for (Stochastic) Gradient Methods.

Introduction to optimization methods for training SciML models Handbook of Convergence Theorems for (Stochastic) Gradient Methods

Reference 2021

Resolution: unresolved; no resolver link, observed 2026-08-03T10:27:47.638277Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0db96c88-29d9-47e9-9490-22bd37d1e638 · outbound

This paper cites Multi-level Residual Networks from Dynamical Systems View.

Introduction to optimization methods for training SciML models Multi-level Residual Networks from Dynamical Systems View

Reference 2501

Resolution: unresolved; no resolver link, observed 2026-08-03T10:27:47.544382Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

Observation 2aafa969-9ce3-4d1d-8106-6921052fc3a5 · inbound

Curvature-Aware Optimization for High-Accuracy Physics-Informed Neural Networks cites this paper.

Curvature-Aware Optimization for High-Accuracy Physics-Informed Neural Networks Introduction to optimization methods for training SciML models

Reference 45

Resolution: verified exact; arxiv_id, observed 2026-06-03T02:05:44.900480Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.