Paper Citation Record · LEDGER

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

As of 22 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 2026-08-21T06:32:19.484+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
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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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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

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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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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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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

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

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

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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

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

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

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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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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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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

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

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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

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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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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

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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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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

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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

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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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This paper cites write newline.

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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Observation 6b90acc0-f8e2-456c-8051-1763cf57b370 · inbound

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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