Paper Citation Record · LEDGER

QUEENS: An Open-Source Python Framework for Solver-Independent Analyses of Large-Scale Computational Models

As of 7 August 2026, this Paper Citation Record lists 57 of 57 outbound references and 3 inbound Pith citation observations for arXiv:2508.16316.

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

pith.paper-citation-record.v1
2508.16316 v2

Coverage vector

measured 57 of 57 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-05T17:26:38.708588Z

measured 60 of 60 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00

measured 3 of 3 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-03T06:25:09.365169Z

measured 1 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Reference resolution

57 of 57 outbound references displayed

verified exact13
verified fuzzy21
unresolved19
parse uncertain0
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External citation measurements

0
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

Observation a7c5c6bd-7e26-4fc2-aa65-ade334e97747 · outbound

This paper cites py DOE : The experimental design package for Python.; 2013.

QUEENS: An Open-Source Python Framework for Solver-Independent Analyses of Large-Scale Computational Models py DOE : The experimental design package for Python.; 2013

Reference 1

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Observation ac75c9e6-55f2-409b-b024-2541baf0e003 · outbound

This paper cites Toward SALib 2.0: Advancing the accessibility and interpretability of global sensitivity analyses.

QUEENS: An Open-Source Python Framework for Solver-Independent Analyses of Large-Scale Computational Models Toward SALib 2.0: Advancing the accessibility and interpretability of global sensitivity analyses

Reference 2

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Source-reported events for the cited work

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Observation c7ae19d5-20fd-46f0-8b98-68b6c9620e16 · outbound

This paper cites Chaospy: An open source tool for designing methods of uncertainty quantification.

QUEENS: An Open-Source Python Framework for Solver-Independent Analyses of Large-Scale Computational Models Chaospy: An open source tool for designing methods of uncertainty quantification

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-05T17:26:34.994078Z

Source-reported events for the cited work

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Observation b0507dc4-4ee3-4e03-b197-8e955bc8496a · outbound

This paper cites UQLab : A Framework for Uncertainty Quantification in Matlab.

QUEENS: An Open-Source Python Framework for Solver-Independent Analyses of Large-Scale Computational Models UQLab : A Framework for Uncertainty Quantification in Matlab

Reference 4

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Observation 30f4048e-6299-41a3-9b2c-cc53f727e9e8 · outbound

This paper cites UQpy : A General Purpose Python Package and Development Environment for Uncertainty Quantification.

QUEENS: An Open-Source Python Framework for Solver-Independent Analyses of Large-Scale Computational Models UQpy : A General Purpose Python Package and Development Environment for Uncertainty Quantification

Reference 5

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Source-reported events for the cited work

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Observation 1f0677a2-3a1b-4961-bcc1-4c74014e47d2 · outbound

This paper cites CUQIpy: I.

QUEENS: An Open-Source Python Framework for Solver-Independent Analyses of Large-Scale Computational Models CUQIpy: I

Reference 6

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Source-reported events for the cited work

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Observation 2cae553f-35be-4c7e-b655-49fa892c8ec9 · outbound

This paper cites CUQIpy: II.

QUEENS: An Open-Source Python Framework for Solver-Independent Analyses of Large-Scale Computational Models CUQIpy: II

Reference 7

Resolution: verified exact; doi, observed 2026-08-05T17:26:41.515928Z

Source-reported events for the cited work

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Observation 858bece2-fa16-4de3-8547-a337018f3003 · outbound

This paper cites a \"a si \.

QUEENS: An Open-Source Python Framework for Solver-Independent Analyses of Large-Scale Computational Models a \"a si \

Reference 8

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Observation 05a7273a-69de-4f0f-aaed-007fe9969160 · outbound

This paper cites PyMC: A Modern and Comprehensive Probabilistic Programming Framework in Python.

QUEENS: An Open-Source Python Framework for Solver-Independent Analyses of Large-Scale Computational Models PyMC: A Modern and Comprehensive Probabilistic Programming Framework in Python

Reference 9

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Source-reported events for the cited work

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Observation d7535b5d-0ac2-4884-a6ab-f1b958a78d24 · outbound

This paper cites An Introduction to Sequential Monte Carlo.

QUEENS: An Open-Source Python Framework for Solver-Independent Analyses of Large-Scale Computational Models An Introduction to Sequential Monte Carlo

Reference 10

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Observation aaa7af25-9ef0-4722-b84e-1db990eb40b6 · outbound

This paper cites UM-Bridge : Uncertainty Quantification and Modeling Bridge.

QUEENS: An Open-Source Python Framework for Solver-Independent Analyses of Large-Scale Computational Models UM-Bridge : Uncertainty Quantification and Modeling Bridge

Reference 11

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Observation a5cc5845-f38a-4940-b28b-a4ba105c4869 · outbound

This paper cites Dakota, A Multilevel Parallel Object-Oriented Framework for Design Optimization, Parameter Estimation, Uncertainty Quantification, and Sensitivity Analysis (V.6.16 User's Manual).

QUEENS: An Open-Source Python Framework for Solver-Independent Analyses of Large-Scale Computational Models Dakota, A Multilevel Parallel Object-Oriented Framework for Design Optimization, Parameter Estimation, Uncertainty Quantification, and Sensitivity Analysis (V.6.16 User's Manual)

Reference 12

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Observation 814e5eac-620b-4d65-b24c-8bd308858aaa · outbound

This paper cites OpenTURNS : An Industrial Software for Uncertainty Quantification in Simulation.

QUEENS: An Open-Source Python Framework for Solver-Independent Analyses of Large-Scale Computational Models OpenTURNS : An Industrial Software for Uncertainty Quantification in Simulation

Reference 13

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Observation 3d5a44fb-965d-4fad-bed3-77e2c78cdd8a · outbound

This paper cites EasyVVUQ: A Library for Verification, Validation and Uncertainty Quantification in High Performance Computing.

QUEENS: An Open-Source Python Framework for Solver-Independent Analyses of Large-Scale Computational Models EasyVVUQ: A Library for Verification, Validation and Uncertainty Quantification in High Performance Computing

Reference 14

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Observation a74bda14-3a44-48ac-9ebe-ec52c7b256e2 · outbound

This paper cites Handbook of Monte Carlo Methods.

QUEENS: An Open-Source Python Framework for Solver-Independent Analyses of Large-Scale Computational Models Handbook of Monte Carlo Methods

Reference 15

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Observation b3c172f0-b258-4640-a466-34f90144aef3 · outbound

This paper cites A Comparison of Three Methods for Selecting Values of Input Variables in the Analysis of Output from a Computer Code.

QUEENS: An Open-Source Python Framework for Solver-Independent Analyses of Large-Scale Computational Models A Comparison of Three Methods for Selecting Values of Input Variables in the Analysis of Output from a Computer Code

Reference 16

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Source-reported events for the cited work

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Observation 271604cc-2799-4fe0-ace7-5d5f6f0d9a12 · outbound

This paper cites On the Distribution of Points in a Cube and the Approximate Evaluation of Integrals.

QUEENS: An Open-Source Python Framework for Solver-Independent Analyses of Large-Scale Computational Models On the Distribution of Points in a Cube and the Approximate Evaluation of Integrals

Reference 17

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Source-reported events for the cited work

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Observation a7fbd6ae-fee7-4bd8-be8d-5e5a253bbe13 · outbound

This paper cites A Method for the Solution of Certain Non-Linear Problems in Least Squares.

QUEENS: An Open-Source Python Framework for Solver-Independent Analyses of Large-Scale Computational Models A Method for the Solution of Certain Non-Linear Problems in Least Squares

Reference 18

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Observation c1069db3-f2bb-46f8-8a9c-178170939436 · outbound

This paper cites An Algorithm for Least-Squares Estimation of Nonlinear Parameters.

QUEENS: An Open-Source Python Framework for Solver-Independent Analyses of Large-Scale Computational Models An Algorithm for Least-Squares Estimation of Nonlinear Parameters

Reference 19

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Observation 193a2fa0-570d-4d2f-afc9-c05e05232231 · outbound

This paper cites SciPy 1.0: Fundamental Algorithms for Scientific Computing in Python.

QUEENS: An Open-Source Python Framework for Solver-Independent Analyses of Large-Scale Computational Models SciPy 1.0: Fundamental Algorithms for Scientific Computing in Python

Reference 20

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Observation 798ddb3e-5a96-422d-93b8-876e926298de · outbound

This paper cites Adam: A Method for Stochastic Optimization.; 2017.

QUEENS: An Open-Source Python Framework for Solver-Independent Analyses of Large-Scale Computational Models Adam: A Method for Stochastic Optimization.; 2017

Reference 21

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Observation 71af16c4-c403-497f-a6c8-646247df2338 · outbound

This paper cites Lecture 6.5-rmsprop: Divide the gradient by a running average of its recent magnitude.

QUEENS: An Open-Source Python Framework for Solver-Independent Analyses of Large-Scale Computational Models Lecture 6.5-rmsprop: Divide the gradient by a running average of its recent magnitude

Reference 22

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Observation 5bff8bac-b84d-4e68-94ae-b1d46c7bee90 · outbound

This paper cites A generalized probabilistic learning approach for multi-fidelity uncertainty quantification in complex physical simulations.

QUEENS: An Open-Source Python Framework for Solver-Independent Analyses of Large-Scale Computational Models A generalized probabilistic learning approach for multi-fidelity uncertainty quantification in complex physical simulations

Reference 23

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Observation 311e5150-c7ed-47ab-9377-805fcd79c20e · outbound

This paper cites Multifidelity approaches for uncertainty quantification.

QUEENS: An Open-Source Python Framework for Solver-Independent Analyses of Large-Scale Computational Models Multifidelity approaches for uncertainty quantification

Reference 24

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Observation 7d139f25-9956-487f-8d13-34ba76c0a889 · outbound

This paper cites Towards efficient uncertainty quantification in complex and large-scale biomechanical problems based on a Bayesian multi-fidelity scheme.

QUEENS: An Open-Source Python Framework for Solver-Independent Analyses of Large-Scale Computational Models Towards efficient uncertainty quantification in complex and large-scale biomechanical problems based on a Bayesian multi-fidelity scheme

Reference 25

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Observation cab81312-d1ff-4117-b05c-31d76f91bfe2 · outbound

This paper cites Accurate uncertainty quantification using inaccurate computational models.

QUEENS: An Open-Source Python Framework for Solver-Independent Analyses of Large-Scale Computational Models Accurate uncertainty quantification using inaccurate computational models

Reference 26

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Observation b04130c1-a505-4295-bc57-124fdb524b44 · outbound

This paper cites Factorial Sampling Plans for Preliminary Computational Experiments.

QUEENS: An Open-Source Python Framework for Solver-Independent Analyses of Large-Scale Computational Models Factorial Sampling Plans for Preliminary Computational Experiments

Reference 27

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Observation 8d951293-56fb-4fd8-9c11-c9fe8060fc08 · outbound

This paper cites Sensitivity Estimates for Nonlinear Mathematical Models.

QUEENS: An Open-Source Python Framework for Solver-Independent Analyses of Large-Scale Computational Models Sensitivity Estimates for Nonlinear Mathematical Models

Reference 28

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Observation b2f5b77b-8c8b-4728-b447-104efe15e786 · outbound

This paper cites Global Sensitivity Indices for Nonlinear Mathematical Models and Their Monte Carlo Estimates.

QUEENS: An Open-Source Python Framework for Solver-Independent Analyses of Large-Scale Computational Models Global Sensitivity Indices for Nonlinear Mathematical Models and Their Monte Carlo Estimates

Reference 29

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Observation ae6e3ef2-ff00-4d12-b477-6597ef53b34b · outbound

This paper cites A Bayesian Approach for Global Sensitivity Analysis of ( Multifidelity ) Computer Codes.

QUEENS: An Open-Source Python Framework for Solver-Independent Analyses of Large-Scale Computational Models A Bayesian Approach for Global Sensitivity Analysis of ( Multifidelity ) Computer Codes

Reference 30

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Observation 7f3694bf-2099-4f54-bf8d-565f3e418b32 · outbound

This paper cites Global Sensitivity Analysis Based on Gaussian-process Metamodelling for Complex Biomechanical Problems.

QUEENS: An Open-Source Python Framework for Solver-Independent Analyses of Large-Scale Computational Models Global Sensitivity Analysis Based on Gaussian-process Metamodelling for Complex Biomechanical Problems

Reference 31

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This paper cites Gaussian Processes for Machine Learning.

QUEENS: An Open-Source Python Framework for Solver-Independent Analyses of Large-Scale Computational Models Gaussian Processes for Machine Learning

Reference 32

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Observation d07a6e5f-74dc-4d00-a04e-b242307fa146 · outbound

This paper cites Gaussian Processes for Big Data.

QUEENS: An Open-Source Python Framework for Solver-Independent Analyses of Large-Scale Computational Models Gaussian Processes for Big Data

Reference 33

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This paper cites Bayesian neural networks and density networks.

QUEENS: An Open-Source Python Framework for Solver-Independent Analyses of Large-Scale Computational Models Bayesian neural networks and density networks

Reference 34

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Observation 070c0ecf-b57b-4a29-af48-e3e3519acf8e · outbound

This paper cites The no-u-turn sampler: Adaptively setting path lengths in hamiltonian monte carlo.

QUEENS: An Open-Source Python Framework for Solver-Independent Analyses of Large-Scale Computational Models The no-u-turn sampler: Adaptively setting path lengths in hamiltonian monte carlo

Reference 35

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QUEENS: An Open-Source Python Framework for Solver-Independent Analyses of Large-Scale Computational Models Sequential Monte Carlo samplers

Reference 36

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This paper cites Monte Carlo Gradient Estimation in Machine Learning.

QUEENS: An Open-Source Python Framework for Solver-Independent Analyses of Large-Scale Computational Models Monte Carlo Gradient Estimation in Machine Learning

Reference 37

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This paper cites Variational Inference: A Review for Statisticians.

QUEENS: An Open-Source Python Framework for Solver-Independent Analyses of Large-Scale Computational Models Variational Inference: A Review for Statisticians

Reference 38

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QUEENS: An Open-Source Python Framework for Solver-Independent Analyses of Large-Scale Computational Models Stochastic Variational Inference

Reference 39

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This paper cites Variational Dropout and the Local Reparameterization Trick.

QUEENS: An Open-Source Python Framework for Solver-Independent Analyses of Large-Scale Computational Models Variational Dropout and the Local Reparameterization Trick

Reference 40

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This paper cites Sticking the Landing: Simple, Lower-Variance Gradient Estimators for Variational Inference.

QUEENS: An Open-Source Python Framework for Solver-Independent Analyses of Large-Scale Computational Models Sticking the Landing: Simple, Lower-Variance Gradient Estimators for Variational Inference

Reference 41

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QUEENS: An Open-Source Python Framework for Solver-Independent Analyses of Large-Scale Computational Models Black box variational inference

Reference 42

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This paper cites Efficient Gradient-Free Variational Inference using Policy Search.

QUEENS: An Open-Source Python Framework for Solver-Independent Analyses of Large-Scale Computational Models Efficient Gradient-Free Variational Inference using Policy Search

Reference 43

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This paper cites Solving Bayesian inverse problems with expensive likelihoods using constrained Gaussian processes and active learning.

QUEENS: An Open-Source Python Framework for Solver-Independent Analyses of Large-Scale Computational Models Solving Bayesian inverse problems with expensive likelihoods using constrained Gaussian processes and active learning

Reference 44

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QUEENS: An Open-Source Python Framework for Solver-Independent Analyses of Large-Scale Computational Models -2pt, ed

Reference 45

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Observation 32c9721b-3759-4428-ad3b-f25df97a4100 · outbound

This paper cites Global sensitivity analysis of a homogenized constrained mixture model of arterial growth and remodeling.

QUEENS: An Open-Source Python Framework for Solver-Independent Analyses of Large-Scale Computational Models Global sensitivity analysis of a homogenized constrained mixture model of arterial growth and remodeling

Reference 46

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This paper cites A novel physics-based and data-supported microstructure model for part-scale simulation of laser powder bed fusion of Ti-6Al-4V.

QUEENS: An Open-Source Python Framework for Solver-Independent Analyses of Large-Scale Computational Models A novel physics-based and data-supported microstructure model for part-scale simulation of laser powder bed fusion of Ti-6Al-4V

Reference 47

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Observation 4144c652-2775-40c6-aebe-231aeb918642 · outbound

This paper cites Physics-based modeling and predictive simulation of powder bed fusion additive manufacturing across length scales.

QUEENS: An Open-Source Python Framework for Solver-Independent Analyses of Large-Scale Computational Models Physics-based modeling and predictive simulation of powder bed fusion additive manufacturing across length scales

Reference 48

Resolution: verified exact; doi, observed 2026-08-05T17:26:39.200842Z

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Observation 091a044d-46a5-4f55-b689-84cc06d6767e · outbound

This paper cites Inverse analysis of material parameters in coupled multi-physics biofilm models.

QUEENS: An Open-Source Python Framework for Solver-Independent Analyses of Large-Scale Computational Models Inverse analysis of material parameters in coupled multi-physics biofilm models

Reference 49

Resolution: verified exact; doi, observed 2026-08-05T17:26:39.087280Z

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This paper cites Validation and parameter optimization of a hybrid embedded/homogenized solid tumor perfusion model.

QUEENS: An Open-Source Python Framework for Solver-Independent Analyses of Large-Scale Computational Models Validation and parameter optimization of a hybrid embedded/homogenized solid tumor perfusion model

Reference 50

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This paper cites Bayesian calibration of coupled computational mechanics models under uncertainty based on interface deformation.

QUEENS: An Open-Source Python Framework for Solver-Independent Analyses of Large-Scale Computational Models Bayesian calibration of coupled computational mechanics models under uncertainty based on interface deformation

Reference 51

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This paper cites Tumour growth: An approach to calibrate parameters of a multiphase porous media model based on in vitro observations of Neuroblastoma spheroid growth in a hydrogel microenvironment.

QUEENS: An Open-Source Python Framework for Solver-Independent Analyses of Large-Scale Computational Models Tumour growth: An approach to calibrate parameters of a multiphase porous media model based on in vitro observations of Neuroblastoma spheroid growth in a hydrogel microenvironment

Reference 52

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This paper cites Dask: Library for dynamic task scheduling.; 2016.

QUEENS: An Open-Source Python Framework for Solver-Independent Analyses of Large-Scale Computational Models Dask: Library for dynamic task scheduling.; 2016

Reference 53

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This paper cites PBS : A Unified Priority-Based Scheduler.

QUEENS: An Open-Source Python Framework for Solver-Independent Analyses of Large-Scale Computational Models PBS : A Unified Priority-Based Scheduler

Reference 54

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This paper cites SLURM : Simple Linux Utility for Resource Management.

QUEENS: An Open-Source Python Framework for Solver-Independent Analyses of Large-Scale Computational Models SLURM : Simple Linux Utility for Resource Management

Reference 55

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QUEENS: An Open-Source Python Framework for Solver-Independent Analyses of Large-Scale Computational Models write newline

Reference 56

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QUEENS: An Open-Source Python Framework for Solver-Independent Analyses of Large-Scale Computational Models write newline

Reference 57

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Pith citing papers

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A Framework for the Bayesian Calibration of Complex and Data-Scarce Models in Applied Sciences cites this paper.

A Framework for the Bayesian Calibration of Complex and Data-Scarce Models in Applied Sciences QUEENS: An Open-Source Python Framework for Solver-Independent Analyses of Large-Scale Computational Models

Reference 33

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Scalable High-Dimensional Bayesian Field Reconstruction with Finite Elements: Application to 3D Porous Media Flow cites this paper.

Scalable High-Dimensional Bayesian Field Reconstruction with Finite Elements: Application to 3D Porous Media Flow QUEENS: An Open-Source Python Framework for Solver-Independent Analyses of Large-Scale Computational Models

Reference 75

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Efficient Bayesian Optimal Experimental Design for Expensive Computational Models over Finite Design Sets cites this paper.

Efficient Bayesian Optimal Experimental Design for Expensive Computational Models over Finite Design Sets QUEENS: An Open-Source Python Framework for Solver-Independent Analyses of Large-Scale Computational Models

Reference 50

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