EngiBench: A Framework for Data-Driven Engineering Design Research | Paper Citation Record

Observation 7884ee78-e7ec-4d32-8c09-f1f6dd8a2b52 · outbound

This paper cites http://ngspice.sourceforge.net, 2025.

EngiBench: A Framework for Data-Driven Engineering Design Research http://ngspice.sourceforge.net, 2025

Reference 1

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This paper cites Lazarov, and Ole Sigmund.

EngiBench: A Framework for Data-Driven Engineering Design Research Lazarov, and Ole Sigmund

Reference 2

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This paper cites Botorch: A framework for efficient monte-carlo bayesian optimization.

EngiBench: A Framework for Data-Driven Engineering Design Research Botorch: A framework for efficient monte-carlo bayesian optimization

Reference 3

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This paper cites Gantl: Toward practical and real-time topology optimization with conditional generative adversarial networks and transfer learning.

EngiBench: A Framework for Data-Driven Engineering Design Research Gantl: Toward practical and real-time topology optimization with conditional generative adversarial networks and transfer learning

Reference 4

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Observation f33691e9-b62a-4db9-8c1e-de2dc82f3a66 · outbound

This paper cites Optimal shape design as a material distribution problem.

EngiBench: A Framework for Data-Driven Engineering Design Research Optimal shape design as a material distribution problem

Reference 5

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Observation e1649021-0f10-4309-a782-43ce22c95b1c · outbound

This paper cites Topology optimization: theory, methods, and applications.

EngiBench: A Framework for Data-Driven Engineering Design Research Topology optimization: theory, methods, and applications

Reference 6

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This paper cites Experiment tracking with weights and biases, 2020.

EngiBench: A Framework for Data-Driven Engineering Design Research Experiment tracking with weights and biases, 2020

Reference 7

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This paper cites Blank and K.

EngiBench: A Framework for Data-Driven Engineering Design Research Blank and K

Reference 8

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Observation 23f0fcd8-665d-480e-bbd9-a2f896f8e695 · outbound

This paper cites Inverse design of two-dimensional airfoils using conditional generative models and surrogate log-likelihoods.

EngiBench: A Framework for Data-Driven Engineering Design Research Inverse design of two-dimensional airfoils using conditional generative models and surrogate log-likelihoods

Reference 9

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Observation 482da8c3-3b48-43bd-8805-6a1971aa10ca · outbound

This paper cites Aerodynamic Design Optimization and Shape Exploration using Generative Adversarial Networks.

EngiBench: A Framework for Data-Driven Engineering Design Research Aerodynamic Design Optimization and Shape Exploration using Generative Adversarial Networks

Reference 10

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EngiBench: A Framework for Data-Driven Engineering Design Research Unresolved cited work

Reference 11

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EngiBench: A Framework for Data-Driven Engineering Design Research Unresolved cited work

Reference 12

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Observation 49f74669-007b-4dcd-9153-0d739cc11f8a · outbound

This paper cites Optimizing diffusion to diffuse optimal designs.

EngiBench: A Framework for Data-Driven Engineering Design Research Optimizing diffusion to diffuse optimal designs

Reference 13

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Observation c1914da8-c149-43ef-b8ac-4866f2d758ce · outbound

This paper cites Hyperparameters in Reinforcement Learning and How To Tune Them.

EngiBench: A Framework for Data-Driven Engineering Design Research Hyperparameters in Reinforcement Learning and How To Tune Them

Reference 14

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Observation 65c5e5f6-1847-452b-930a-0aa251d03b6b · outbound

This paper cites Drivaernet++: A large-scale multimodal car dataset with computational fluid dynamics simulations and deep learning benchmarks.

EngiBench: A Framework for Data-Driven Engineering Design Research Drivaernet++: A large-scale multimodal car dataset with computational fluid dynamics simulations and deep learning benchmarks

Reference 15

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Observation f32f7605-9808-4c96-887f-1d1d94a406c5 · outbound

This paper cites Topology optimization of heat exchangers: A review.

EngiBench: A Framework for Data-Driven Engineering Design Research Topology optimization of heat exchangers: A review

Reference 16

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Observation f76c3148-eccd-4881-8dcc-4a7cc705a2f2 · outbound

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EngiBench: A Framework for Data-Driven Engineering Design Research Unresolved cited work

Reference 17

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Observation 7ecc80cb-9883-494f-9bf2-1262f461d3e1 · outbound

This paper cites Hyperparameter Optimization for Multi-Objective Reinforcement Learning.

EngiBench: A Framework for Data-Driven Engineering Design Research Hyperparameter Optimization for Multi-Objective Reinforcement Learning

Reference 18

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Observation 9c156693-9954-4392-8135-16af43ef945a · outbound

This paper cites MOMAland: A Set of Benchmarks for Multi-Objective Multi-Agent Reinforcement Learning.

EngiBench: A Framework for Data-Driven Engineering Design Research MOMAland: A Set of Benchmarks for Multi-Objective Multi-Agent Reinforcement Learning

Reference 19

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EngiBench: A Framework for Data-Driven Engineering Design Research Unresolved cited work

Reference 20

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This paper cites Multi-material thermo- mechanical topology optimization with applications to additive manufacturing: Design of main composite part and its support structure.

EngiBench: A Framework for Data-Driven Engineering Design Research Multi-material thermo- mechanical topology optimization with applications to additive manufacturing: Design of main composite part and its support structure

Reference 21

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This paper cites Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio.

EngiBench: A Framework for Data-Driven Engineering Design Research Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio

Reference 22

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This paper cites A kernel two-sample test.

EngiBench: A Framework for Data-Driven Engineering Design Research A kernel two-sample test

Reference 23

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EngiBench: A Framework for Data-Driven Engineering Design Research Unresolved cited work

Reference 24

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Observation e1a3d43e-ee5d-4be2-b3e2-1f187ec95147 · outbound

This paper cites Mean squared error may lead you astray when optimizing your inverse design methods.

EngiBench: A Framework for Data-Driven Engineering Design Research Mean squared error may lead you astray when optimizing your inverse design methods

Reference 25

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Observation b6e89686-63d5-4b7d-9f49-917f050df43a · outbound

This paper cites Hajdik, Anil Yildirim, Neil Wu, Benjamin J.

EngiBench: A Framework for Data-Driven Engineering Design Research Hajdik, Anil Yildirim, Neil Wu, Benjamin J

Reference 26

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Observation 1fd61796-f71b-4c6b-b62c-3598064beb50 · outbound

This paper cites Harris, K.

EngiBench: A Framework for Data-Driven Engineering Design Research Harris, K

Reference 27

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This paper cites BubbleML: A Multi-Physics Dataset and Benchmarks for Machine Learning.

EngiBench: A Framework for Data-Driven Engineering Design Research BubbleML: A Multi-Physics Dataset and Benchmarks for Machine Learning

Reference 28

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This paper cites Mader, Anil Yildirim, and Joaquim R.

EngiBench: A Framework for Data-Driven Engineering Design Research Mader, Anil Yildirim, and Joaquim R

Reference 29

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This paper cites Denoising Diffusion Probabilistic Models.

EngiBench: A Framework for Data-Driven Engineering Design Research Denoising Diffusion Probabilistic Models

Reference 30

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EngiBench: A Framework for Data-Driven Engineering Design Research Unresolved cited work

Reference 31

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This paper cites Adjoint method and inverse design for nonlinear nanophotonic devices.

EngiBench: A Framework for Data-Driven Engineering Design Research Adjoint method and inverse design for nonlinear nanophotonic devices

Reference 32

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This paper cites Forward-mode differentiation of maxwell’s equations.

EngiBench: A Framework for Data-Driven Engineering Design Research Forward-mode differentiation of maxwell’s equations

Reference 33

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EngiBench: A Framework for Data-Driven Engineering Design Research Unresolved cited work

Reference 34

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This paper cites Shiphullgan: A generic parametric modeller for ship hull design using deep convolutional generative model.

EngiBench: A Framework for Data-Driven Engineering Design Research Shiphullgan: A generic parametric modeller for ship hull design using deep convolutional generative model

Reference 35

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This paper cites Learning multiple layers of features from tiny images.

EngiBench: A Framework for Data-Driven Engineering Design Research Learning multiple layers of features from tiny images

Reference 36

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This paper cites Determinantal point processes for machine learning.

EngiBench: A Framework for Data-Driven Engineering Design Research Determinantal point processes for machine learning

Reference 37

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This paper cites Kurtzer, cclerget, Michael Bauer, Ian Kaneshiro, David Trudgian, and David Godlove.

EngiBench: A Framework for Data-Driven Engineering Design Research Kurtzer, cclerget, Michael Bauer, Ian Kaneshiro, David Trudgian, and David Godlove

Reference 38

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This paper cites Mnist handwritten digit database.

EngiBench: A Framework for Data-Driven Engineering Design Research Mnist handwritten digit database

Reference 39

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Observation 43599528-69b5-4866-a97b-d383b25691c9 · outbound

This paper cites Datasets: A Community Library for Natural Language Processing.

EngiBench: A Framework for Data-Driven Engineering Design Research Datasets: A Community Library for Natural Language Processing

Reference 40

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EngiBench: A Framework for Data-Driven Engineering Design Research Unresolved cited work

Reference 41

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This paper cites Automated Solution of Differential Equations by the Finite Element Method: The FEniCS Book, volume 84 of Lecture Notes in Computational Science and Engineering.

EngiBench: A Framework for Data-Driven Engineering Design Research Automated Solution of Differential Equations by the Finite Element Method: The FEniCS Book, volume 84 of Lecture Notes in Computational Science and Engineering

Reference 42

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Observation 344da6fc-c501-4f0d-9512-2b83d5f0f7dd · outbound

This paper cites Mader, Gaetan K.

EngiBench: A Framework for Data-Driven Engineering Design Research Mader, Gaetan K

Reference 43

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This paper cites Aerodynamic design optimization: Challenges and perspectives.

EngiBench: A Framework for Data-Driven Engineering Design Research Aerodynamic design optimization: Challenges and perspectives

Reference 44

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EngiBench: A Framework for Data-Driven Engineering Design Research Unresolved cited work

Reference 45

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This paper cites Dimensionless group-driven ensemble neural networks for robust design optimization in engineering.

EngiBench: A Framework for Data-Driven Engineering Design Research Dimensionless group-driven ensemble neural networks for robust design optimization in engineering

Reference 46

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Observation c2d2d103-4432-4e2e-baa6-02d3c68c344f · outbound

This paper cites An Integrated Approach to Designing Robust Gas-Bearing Supported Turbocompressors Through Surrogate Modeling and Constrained All-At-Once Multi-Objective Optimization.

EngiBench: A Framework for Data-Driven Engineering Design Research An Integrated Approach to Designing Robust Gas-Bearing Supported Turbocompressors Through Surrogate Modeling and Constrained All-At-Once Multi-Objective Optimization

Reference 47

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Observation 263af20e-6a2f-4eb4-a497-be076e3d15fa · outbound

This paper cites Robust design optimization of gas-lubricated herringbone grooved journal bearings: Surrogate modeling and experimental validation.

EngiBench: A Framework for Data-Driven Engineering Design Research Robust design optimization of gas-lubricated herringbone grooved journal bearings: Surrogate modeling and experimental validation

Reference 48

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Observation dd3693b9-ee92-4e17-8e8f-6c5a671337db · outbound

This paper cites From topology optimization design to additive manufacturing: Today’s success and tomorrow’s roadmap.

EngiBench: A Framework for Data-Driven Engineering Design Research From topology optimization design to additive manufacturing: Today’s success and tomorrow’s roadmap

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:52:09.301866Z

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Observation c4198237-2689-4f71-8a49-b31bd3253495 · outbound

This paper cites Docker: lightweight linux containers for consistent development and deployment.

EngiBench: A Framework for Data-Driven Engineering Design Research Docker: lightweight linux containers for consistent development and deployment

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:52:09.191762Z

Source-reported events for the cited work

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Observation f5295246-de35-4146-80da-4c0083c7cf20 · outbound

This paper cites Conditional Generative Adversarial Nets.

EngiBench: A Framework for Data-Driven Engineering Design Research Conditional Generative Adversarial Nets

Reference 51

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Observation cb545f3b-7556-46b1-8bdb-3782ad6b1641 · outbound

This paper cites dolfin-adjoint 2018.1: automated adjoints for fenics and firedrake.

EngiBench: A Framework for Data-Driven Engineering Design Research dolfin-adjoint 2018.1: automated adjoints for fenics and firedrake

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:52:09.080005Z

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Observation 098fa811-66c2-4280-a0e6-ba87813bab7c · outbound

This paper cites Topology optimization of cooling plates for battery thermal management.

EngiBench: A Framework for Data-Driven Engineering Design Research Topology optimization of cooling plates for battery thermal management

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:52:08.966164Z

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.

Observation e2ea3f0c-dc36-415e-8b42-3ebfd9c3ed7a · outbound

This paper cites The well: a large-scale collection of diverse physics simulations for machine learning.

EngiBench: A Framework for Data-Driven Engineering Design Research The well: a large-scale collection of diverse physics simulations for machine learning

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:52:08.825291Z

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.

Observation 06bb5366-4532-4fcb-a0e1-1fe9fd507c7e · outbound

This paper cites Automated Reinforcement Learning (AutoRL): A Survey and Open Problems.

EngiBench: A Framework for Data-Driven Engineering Design Research Automated Reinforcement Learning (AutoRL): A Survey and Open Problems

Reference 55

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Observation aa05538d-424f-42a0-b809-6b13b6817bf8 · outbound

This paper cites PyTorch: An Imperative Style, High- Performance Deep Learning Library.

EngiBench: A Framework for Data-Driven Engineering Design Research PyTorch: An Imperative Style, High- Performance Deep Learning Library

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:52:08.762302Z

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Observation 19fc915d-15f7-45ab-8b04-a818e12cedee · outbound

This paper cites Inverse design and demonstration of a compact and broadband on-chip wavelength demultiplexer.

EngiBench: A Framework for Data-Driven Engineering Design Research Inverse design and demonstration of a compact and broadband on-chip wavelength demultiplexer

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:52:08.675784Z

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

Observation 6dd52f8a-2e03-4d60-85d0-fa48c3db9666 · outbound

This paper cites Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks.

EngiBench: A Framework for Data-Driven Engineering Design Research Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks

Reference 58

Resolution: unresolved; no resolver link, observed 2026-08-07T11:52:05.256976Z

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Observation 26926fdd-c7e9-4ae5-95f8-2a93954d7ba6 · outbound

This paper cites BIKED: A Dataset for Computational Bicycle Design With Machine Learning Benchmarks.

EngiBench: A Framework for Data-Driven Engineering Design Research BIKED: A Dataset for Computational Bicycle Design With Machine Learning Benchmarks

Reference 59

Resolution: verified exact; doi, observed 2026-08-07T11:52:06.648891Z

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Observation f22fc9c1-b48d-41bb-9b10-6db34768413f · outbound

This paper cites Deep generative models in engineering design: A review.

EngiBench: A Framework for Data-Driven Engineering Design Research Deep generative models in engineering design: A review

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:52:08.597804Z

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

Observation 0e7cc88d-b9d7-4fd5-b253-3e03bfa8b673 · outbound

This paper cites Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design.

EngiBench: A Framework for Data-Driven Engineering Design Research Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design

Reference 61

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Observation f967d5b7-18c1-4d13-9a62-a9a3ab04c17e · outbound

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EngiBench: A Framework for Data-Driven Engineering Design Research Unresolved cited work

Reference 62

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Observation edba640c-02af-409f-af99-9a0af8af460b · outbound

This paper cites Morphology-based black and white filters for topology optimization.

EngiBench: A Framework for Data-Driven Engineering Design Research Morphology-based black and white filters for topology optimization

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:52:08.431510Z

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Observation 70b23a5d-8233-476f-8d9e-b3af2ef8c575 · outbound

This paper cites Practical Bayesian Optimization of Machine Learning Algorithms.

EngiBench: A Framework for Data-Driven Engineering Design Research Practical Bayesian Optimization of Machine Learning Algorithms

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:52:08.360544Z

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Observation 5a4acbcc-6bba-462a-82e5-c0d781a7a0ec · outbound

This paper cites Neural networks for topology optimization.

EngiBench: A Framework for Data-Driven Engineering Design Research Neural networks for topology optimization

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:52:08.286630Z

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Observation 164018ef-96b7-4fe3-8ea0-ba014579bd27 · outbound

This paper cites Pdebench: An extensive benchmark for scientific machine learning.Advances in Neural Information Processing Systems, 35:1596–1611, 2022.

EngiBench: A Framework for Data-Driven Engineering Design Research Pdebench: An extensive benchmark for scientific machine learning.Advances in Neural Information Processing Systems, 35:1596–1611, 2022

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:52:08.231096Z

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.

Observation 65f566ba-acb6-4472-bd26-2fae4acf0d84 · outbound

This paper cites Topology optimization of nonlinear heat conduction problems involving large temperature gradient.

EngiBench: A Framework for Data-Driven Engineering Design Research Topology optimization of nonlinear heat conduction problems involving large temperature gradient

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:52:08.179496Z

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

Observation 5bb0f2c6-a9de-4c3b-a94c-35c324957e76 · outbound

This paper cites Lagrangebench: A lagrangian fluid mechanics benchmarking suite.

EngiBench: A Framework for Data-Driven Engineering Design Research Lagrangebench: A lagrangian fluid mechanics benchmarking suite

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:52:08.106957Z

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

Observation 5bb94bc2-890f-45e2-a2e1-d04725bed52f · outbound

This paper cites Gymnasium: A Standard Interface for Reinforcement Learning Environments.

EngiBench: A Framework for Data-Driven Engineering Design Research Gymnasium: A Standard Interface for Reinforcement Learning Environments

Reference 69

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Source-reported events for the cited work

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Observation 054cc908-7494-46da-8a29-ad7cc580464e · outbound

This paper cites Diffusers: State-of-the-art diffusion models.

EngiBench: A Framework for Data-Driven Engineering Design Research Diffusers: State-of-the-art diffusion models

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:52:08.015623Z

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.

Observation 9c10b548-fb66-4b5a-8dd4-d3f02f2bc1d3 · outbound

This paper cites On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming.

EngiBench: A Framework for Data-Driven Engineering Design Research On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:52:07.935723Z

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.

Observation 0045438b-e8b6-47c4-aa98-a2a0ac6d7f6a · outbound

This paper cites Mader, John Jasa, and Joaquim R.

EngiBench: A Framework for Data-Driven Engineering Design Research Mader, John Jasa, and Joaquim R

Reference 73

Resolution: unresolved; no resolver link, observed 2026-08-07T11:52:06.172757Z

Source-reported events for the cited work

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Observation 777d38f0-1763-4606-af0e-90dd74bc0597 · outbound

This paper cites Deep ensemble neural-like P systems for segmentation of central serous chorioretinopathy lesion.

EngiBench: A Framework for Data-Driven Engineering Design Research Deep ensemble neural-like P systems for segmentation of central serous chorioretinopathy lesion

Reference 74

Resolution: verified exact; doi, observed 2026-08-07T11:52:06.569882Z

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.

Observation 476d56f7-65f3-4ec9-b04a-04ce71c3bda8 · outbound

This paper cites A jacobian-free approximate newton–krylov startup strategy for rans simulations.

EngiBench: A Framework for Data-Driven Engineering Design Research A jacobian-free approximate newton–krylov startup strategy for rans simulations

Reference 75

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:52:07.858803Z

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.

Observation 4addbc04-21a0-4200-a6d3-6b88a119cf16 · outbound

This paper cites compliance.

EngiBench: A Framework for Data-Driven Engineering Design Research compliance

Reference 76

Resolution: unresolved; no resolver link, observed 2026-08-07T11:52:06.374046Z

Source-reported events for the cited work

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Observation 65d1f22d-0ca1-4bb7-8cda-d8b495fbe7c1 · outbound

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EngiBench: A Framework for Data-Driven Engineering Design Research Unresolved cited work

Reference 79

Resolution: unresolved; raw_fallback, observed 2026-08-07T11:52:07.774782Z

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.

Observation 5c85e8ed-2702-45f1-a7c9-89000565aabe · outbound

This paper cites an unresolved cited work.

EngiBench: A Framework for Data-Driven Engineering Design Research Unresolved cited work

Reference 80

Resolution: unresolved; raw_fallback, observed 2026-08-07T11:52:07.666853Z

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

Observation f90aa235-b071-40c0-b1a6-5965a96d3eef · outbound

This paper cites an unresolved cited work.

EngiBench: A Framework for Data-Driven Engineering Design Research Unresolved cited work

Reference 81

Resolution: unresolved; raw_fallback, observed 2026-08-07T11:52:07.585956Z

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.

Observation ca5a7380-930c-4f2f-890b-d4d0de97375a · outbound

This paper cites an unresolved cited work.

EngiBench: A Framework for Data-Driven Engineering Design Research Unresolved cited work

Reference 82

Resolution: unresolved; raw_fallback, observed 2026-08-07T11:52:07.487470Z

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.

Observation 36646c68-c184-4fb4-beb1-fcd93f456e8c · outbound

This paper cites doi: 10.1093/jcde/qwaf056.

EngiBench: A Framework for Data-Driven Engineering Design Research doi: 10.1093/jcde/qwaf056

Reference 2025

Resolution: verified exact; doi, observed 2026-08-07T11:52:06.734060Z

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.

Observation 4aefb8e4-68ef-4b62-b2a1-534c1d0c2c27 · outbound

This paper cites URL https://www.degruyter.com/document/doi/10.1515/ rnam-2019-0018/html.

EngiBench: A Framework for Data-Driven Engineering Design Research URL https://www.degruyter.com/document/doi/10.1515/ rnam-2019-0018/html

Reference 3988

Resolution: unresolved; no resolver link, observed 2026-08-07T11:52:05.820195Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Reference resolution

Outbound references

Pith citing papers