Paper Citation Record · LEDGER

Active Learning Enhanced Surrogate Modeling of Jet Engines in JuliaSim

As of 23 August 2026, this Paper Citation Record lists 32 of 32 outbound references and 1 inbound Pith citation observation for arXiv:2501.07701.

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

pith.paper-citation-record.v1
2501.07701 v1

Coverage vector

measured 32 of 32 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-10T20:39:10.416321Z

measured 33 of 33 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-23T06:30:58.430688+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-02T23:34:41.136108Z

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

32 of 32 outbound references displayed

verified exact1
verified fuzzy29
unresolved2
parse uncertain0
malformed identifier0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation e2749070-bf7d-46aa-ab84-64f1e5b564c2 · outbound

This paper cites A review on design of experiments and surrogate models in aircraft real-time and many-query aerodynamic analyses,.

Active Learning Enhanced Surrogate Modeling of Jet Engines in JuliaSim A review on design of experiments and surrogate models in aircraft real-time and many-query aerodynamic analyses,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:39:10.993924Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation d241da90-5a60-4977-a845-57f3c8ef6548 · outbound

This paper cites Application of deep learning based multi-fidelity surrogate model to robust aerodynamic design optimization,.

Active Learning Enhanced Surrogate Modeling of Jet Engines in JuliaSim Application of deep learning based multi-fidelity surrogate model to robust aerodynamic design optimization,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:39:10.977541Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 8620d86a-d194-4937-94fc-acb0dac277fd · outbound

This paper cites Approaches for quantifying uncertainties in computational modeling for aerospace applications,.

Active Learning Enhanced Surrogate Modeling of Jet Engines in JuliaSim Approaches for quantifying uncertainties in computational modeling for aerospace applications,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:39:10.963317Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 7d13b6ef-f360-4912-a193-8568d7934c41 · outbound

This paper cites Uncertaintyquantificationinaeroelasticity,.

Active Learning Enhanced Surrogate Modeling of Jet Engines in JuliaSim Uncertaintyquantificationinaeroelasticity,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:39:10.949401Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation a7397d02-0950-4545-8333-6eb81932c92f · outbound

This paper cites Using surrogate models and response surfaces in structural optimization–with application to crashworthiness design and sheet metal forming,.

Active Learning Enhanced Surrogate Modeling of Jet Engines in JuliaSim Using surrogate models and response surfaces in structural optimization–with application to crashworthiness design and sheet metal forming,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:39:10.935396Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 5e00de41-a6ad-4deb-9c97-9c24f2693a9b · outbound

This paper cites Building efficient response surfaces of aerodynamic functions with kriging and cokriging,.

Active Learning Enhanced Surrogate Modeling of Jet Engines in JuliaSim Building efficient response surfaces of aerodynamic functions with kriging and cokriging,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:39:10.920638Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 19900be0-1ce6-4eb1-b609-c5570496747a · outbound

This paper cites Mesh deformation using radial basis functions for gradient-based aerodynamic shape optimization,.

Active Learning Enhanced Surrogate Modeling of Jet Engines in JuliaSim Mesh deformation using radial basis functions for gradient-based aerodynamic shape optimization,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:39:10.904826Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 893988b8-ba58-4094-9e56-084cd74d9e3c · outbound

This paper cites Deep neural network for unsteady aerodynamic and aeroelastic modeling across multiple Mach numbers,.

Active Learning Enhanced Surrogate Modeling of Jet Engines in JuliaSim Deep neural network for unsteady aerodynamic and aeroelastic modeling across multiple Mach numbers,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:39:10.889126Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 8f6f9ffd-fdff-4984-974a-241ea39c297b · outbound

This paper cites Robust design optimization using surrogate models,.

Active Learning Enhanced Surrogate Modeling of Jet Engines in JuliaSim Robust design optimization using surrogate models,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:39:10.874175Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 8473d61c-b70a-4c60-9e1f-c876e390bacd · outbound

This paper cites Surrogate model-based optimization framework: a case study in aerospace design,.

Active Learning Enhanced Surrogate Modeling of Jet Engines in JuliaSim Surrogate model-based optimization framework: a case study in aerospace design,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:39:10.859775Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 3a3aa13b-afef-4677-bec7-9e78e855d0b5 · outbound

This paper cites Surrogate model selection for design space approximation and surrogatebased optimization,.

Active Learning Enhanced Surrogate Modeling of Jet Engines in JuliaSim Surrogate model selection for design space approximation and surrogatebased optimization,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:39:10.845192Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation e380c1fe-3b0b-40d9-8a23-4a0135729e5a · outbound

This paper cites Advantages of surrogate models for architectural design optimization,.

Active Learning Enhanced Surrogate Modeling of Jet Engines in JuliaSim Advantages of surrogate models for architectural design optimization,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:39:10.827764Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 1b4c7c21-2ecc-496d-abd0-c7fd02ca4466 · outbound

This paper cites Surrogate-based optimization,.

Active Learning Enhanced Surrogate Modeling of Jet Engines in JuliaSim Surrogate-based optimization,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:39:10.810224Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 5eba6b19-134a-42f3-af16-13babdffe4e9 · outbound

This paper cites Managing computational complexity using surrogate models: a critical review,.

Active Learning Enhanced Surrogate Modeling of Jet Engines in JuliaSim Managing computational complexity using surrogate models: a critical review,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:39:10.795011Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 486fe0e0-5cb7-4f39-bd0c-4a6b7a261e29 · outbound

This paper cites Surrogate model uncertainty quantification for reliability-based design optimization,.

Active Learning Enhanced Surrogate Modeling of Jet Engines in JuliaSim Surrogate model uncertainty quantification for reliability-based design optimization,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:39:10.776760Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 47296dba-0277-44c5-9c68-4ebd9279f277 · outbound

This paper cites Numerical propulsion system simulation (NPSS) 1999 industry review,.

Active Learning Enhanced Surrogate Modeling of Jet Engines in JuliaSim Numerical propulsion system simulation (NPSS) 1999 industry review,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:39:10.760650Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation f484f8fe-7001-418b-ad2b-7939bdaaed05 · outbound

This paper cites Scientific Machine Learning (SciML) Surrogates for Industry, Part 1: The Guiding Questions,.

Active Learning Enhanced Surrogate Modeling of Jet Engines in JuliaSim Scientific Machine Learning (SciML) Surrogates for Industry, Part 1: The Guiding Questions,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:39:10.744527Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 41c2ca1d-8f3c-48ca-9b01-4149e363501d · outbound

This paper cites Composing modeling and simulation with machine learning in Julia,.

Active Learning Enhanced Surrogate Modeling of Jet Engines in JuliaSim Composing modeling and simulation with machine learning in Julia,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:39:10.725600Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 424ba073-a344-42f2-98bc-80c0186fdca6 · outbound

This paper cites Julia: A fresh approach to numerical computing,.

Active Learning Enhanced Surrogate Modeling of Jet Engines in JuliaSim Julia: A fresh approach to numerical computing,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:39:10.711053Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation c1279eb2-aa05-4811-9759-fb806669948e · outbound

This paper cites Accelerating Simulation of Stiff Nonlinear Systems using Continuous-Time Echo State Networks.

Active Learning Enhanced Surrogate Modeling of Jet Engines in JuliaSim Accelerating Simulation of Stiff Nonlinear Systems using Continuous-Time Echo State Networks

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-10T20:39:10.360829Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ad7f8e7d-21a7-48f2-81d3-768fc29b7e9d · outbound

This paper cites Stably accelerating stiff quantitative systems pharmacology models: Continuous-time echo state networks as implicit machine learning,.

Active Learning Enhanced Surrogate Modeling of Jet Engines in JuliaSim Stably accelerating stiff quantitative systems pharmacology models: Continuous-time echo state networks as implicit machine learning,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:39:10.693650Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 26707617-92eb-4b35-be3c-35630525f728 · outbound

This paper cites Composable and reusable neural surrogates to predict system response of causal model components,.

Active Learning Enhanced Surrogate Modeling of Jet Engines in JuliaSim Composable and reusable neural surrogates to predict system response of causal model components,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:39:10.675345Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation c5048269-a78b-45b3-8b12-6f3f45465fc5 · outbound

This paper cites Continuous-time echo state networks for predicting power system dynamics,.

Active Learning Enhanced Surrogate Modeling of Jet Engines in JuliaSim Continuous-time echo state networks for predicting power system dynamics,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:39:10.657592Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 62d6afd2-2caa-43e3-9399-72891690f176 · outbound

This paper cites an unresolved cited work.

Active Learning Enhanced Surrogate Modeling of Jet Engines in JuliaSim Unresolved cited work

Reference 24

Resolution: unresolved; raw_fallback, observed 2026-08-10T20:39:10.640980Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 5f02cb98-12f8-4e74-a6bb-cd1fdc5e9e70 · outbound

This paper cites Active Learning-CFD Integrated Surrogate-Based Framework for Shape Optimization of LTA Systems,.

Active Learning Enhanced Surrogate Modeling of Jet Engines in JuliaSim Active Learning-CFD Integrated Surrogate-Based Framework for Shape Optimization of LTA Systems,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:39:10.615948Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation bde6a575-b5eb-45f2-a2d0-3f89edb9b9a1 · outbound

This paper cites Active learning for efficient data-driven aerodynamic modeling in spaceplane design,.

Active Learning Enhanced Surrogate Modeling of Jet Engines in JuliaSim Active learning for efficient data-driven aerodynamic modeling in spaceplane design,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:39:10.591403Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 1ea36141-2d8d-4d6c-bded-50e62dd650c3 · outbound

This paper cites UnbiasedNets: a dataset diversification framework for robustness bias alleviation in neural networks,.

Active Learning Enhanced Surrogate Modeling of Jet Engines in JuliaSim UnbiasedNets: a dataset diversification framework for robustness bias alleviation in neural networks,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:39:10.571569Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 54806ae3-a748-4359-8c9f-cf504b01ccd3 · outbound

This paper cites MetaBalance: High-Performance Neural Networks for Class-Imbalanced Data.

Active Learning Enhanced Surrogate Modeling of Jet Engines in JuliaSim MetaBalance: High-Performance Neural Networks for Class-Imbalanced Data

Reference 28

Resolution: verified exact; local_arxiv, observed 2026-08-10T20:39:10.464657Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 5c22da29-ecb3-4cc2-8ccd-62c9ea3bd23c · outbound

This paper cites Analysis of euclidean distance and manhattan distance measure in face recognition,.

Active Learning Enhanced Surrogate Modeling of Jet Engines in JuliaSim Analysis of euclidean distance and manhattan distance measure in face recognition,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:39:10.554192Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation b7d2fea2-e691-4c6f-91fa-6cd7f9321814 · outbound

This paper cites On Latin hypercube sampling,.

Active Learning Enhanced Surrogate Modeling of Jet Engines in JuliaSim On Latin hypercube sampling,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:39:10.535574Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation be19acba-1fc6-4ba2-845a-bb1179fda62d · outbound

This paper cites Ensemble of surrogates,.

Active Learning Enhanced Surrogate Modeling of Jet Engines in JuliaSim Ensemble of surrogates,

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:39:10.517523Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 5fee6fce-cd77-48c1-b7dd-c6b3c1605f9c · outbound

This paper cites Mixture of experts: a literature survey,.

Active Learning Enhanced Surrogate Modeling of Jet Engines in JuliaSim Mixture of experts: a literature survey,

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:39:10.501218Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Pith citing papers

Observation 61db564f-4b31-4948-a852-e3c6ff103020 · inbound

Learning Gradient Flow: Using Equation Discovery to Accelerate Engineering Optimization cites this paper.

Learning Gradient Flow: Using Equation Discovery to Accelerate Engineering Optimization Active Learning Enhanced Surrogate Modeling of Jet Engines in JuliaSim

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-02T23:34:41.136108Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.