Surrogate-Based Optimization Techniques for Process Systems Engineering | Paper Citation Record

Observation 8c1c18d3-3fe9-4cc8-8bf9-76c20da99563 · outbound

This paper cites Benchmarking Derivative-Free Optimization Algorithms.

Surrogate-Based Optimization Techniques for Process Systems Engineering Benchmarking Derivative-Free Optimization Algorithms

Reference 1

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Observation 7bdff7e2-e2f9-4a64-806a-267acafabe6a · outbound

This paper cites Data-Driven Optimization for Process Systems Engineering Applications.

Surrogate-Based Optimization Techniques for Process Systems Engineering Data-Driven Optimization for Process Systems Engineering Applications

Reference 2

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Observation bb4788b9-cba9-45b6-9100-fd4a02724db5 · outbound

This paper cites A Perspective on Smart Process Manufacturing Research Challenges for Process Systems Engineers.

Surrogate-Based Optimization Techniques for Process Systems Engineering A Perspective on Smart Process Manufacturing Research Challenges for Process Systems Engineers

Reference 3

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This paper cites Multi-Scale Optimization for Process Systems Engineering.

Surrogate-Based Optimization Techniques for Process Systems Engineering Multi-Scale Optimization for Process Systems Engineering

Reference 4

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Observation b3bb9541-1cb2-400a-9161-e1b940a26b82 · outbound

This paper cites The ALAMO Approach to Machine Learning.

Surrogate-Based Optimization Techniques for Process Systems Engineering The ALAMO Approach to Machine Learning

Reference 5

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Observation fe7e8c87-9abe-47fa-8328-2045729c0fb6 · outbound

This paper cites On the Numerical Performance of Finite-Difference-Based Methods for Derivative- Free Optimization.

Surrogate-Based Optimization Techniques for Process Systems Engineering On the Numerical Performance of Finite-Difference-Based Methods for Derivative- Free Optimization

Reference 6

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Observation 46e2921b-e9e6-4cdc-99f8-387559eaca02 · outbound

This paper cites Complete Search in Continuous Global Optimization and Constraint Satisfaction.

Surrogate-Based Optimization Techniques for Process Systems Engineering Complete Search in Continuous Global Optimization and Constraint Satisfaction

Reference 7

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Observation 66d3bda6-21dd-4ca2-83e8-0b4883eb10b7 · outbound

This paper cites Advances in Surrogate Based Modeling, Feasibility Analysis, and Optimization: A Review.

Surrogate-Based Optimization Techniques for Process Systems Engineering Advances in Surrogate Based Modeling, Feasibility Analysis, and Optimization: A Review

Reference 8

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Observation 39af475b-113f-454b-9297-63b3fb99755e · outbound

This paper cites Machine Learning-Based Surrogate Modeling for Data-Driven Optimiza- tion: A Comparison of Subset Selection for Regression Techniques.

Surrogate-Based Optimization Techniques for Process Systems Engineering Machine Learning-Based Surrogate Modeling for Data-Driven Optimiza- tion: A Comparison of Subset Selection for Regression Techniques

Reference 9

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Observation 6389b840-fa9f-4ab9-818e-943c2445a6ff · outbound

This paper cites Deterministic Global Process Optimization: Accurate (Single-Species) Properties via Artificial Neural Networks.

Surrogate-Based Optimization Techniques for Process Systems Engineering Deterministic Global Process Optimization: Accurate (Single-Species) Properties via Artificial Neural Networks

Reference 10

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Observation 52122769-dffd-49cf-8fea-2882b378c877 · outbound

This paper cites Surrogate optimization of variational quantum circuits.

Surrogate-Based Optimization Techniques for Process Systems Engineering Surrogate optimization of variational quantum circuits

Reference 11

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Observation fa076a43-db70-4b6d-afff-57ef4f6e532b · outbound

This paper cites A Surrogate-based Framework for Feasibility-driven Optimization of Expensive Simulations.

Surrogate-Based Optimization Techniques for Process Systems Engineering A Surrogate-based Framework for Feasibility-driven Optimization of Expensive Simulations

Reference 12

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Observation 3b5bd02c-9fc8-43ed-a5ce-32872a025da9 · outbound

This paper cites Algebraic Surrogate-based Process Optimization Using Bayesian Symbolic Learning.

Surrogate-Based Optimization Techniques for Process Systems Engineering Algebraic Surrogate-based Process Optimization Using Bayesian Symbolic Learning

Reference 13

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Observation e7bb7126-e3e0-4e70-9a3a-58b9bae21fb7 · outbound

This paper cites Surrogate-Based Optimisation of Process Systems to Recover Resources from Wastewater.

Surrogate-Based Optimization Techniques for Process Systems Engineering Surrogate-Based Optimisation of Process Systems to Recover Resources from Wastewater

Reference 15

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Observation 218d1c5c-9e47-480d-9bfd-8f5fcc4654f5 · outbound

This paper cites Bayesian optimization as a flexible and efficient design framework for sustainable process systems.

Surrogate-Based Optimization Techniques for Process Systems Engineering Bayesian optimization as a flexible and efficient design framework for sustainable process systems

Reference 17

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Observation 88eae16d-7cf8-4bd8-8f3e-b901ff60a6e7 · outbound

This paper cites Surrogate Based Optimization of a Process of Polycrystalline Silicon Production.

Surrogate-Based Optimization Techniques for Process Systems Engineering Surrogate Based Optimization of a Process of Polycrystalline Silicon Production

Reference 18

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Observation 01b039ef-f15c-4976-ba97-844f3cf17c2f · outbound

This paper cites A Trust Region Framework for Heat Exchanger Network Synthesis with Detailed Individual Heat Exchanger Designs.

Surrogate-Based Optimization Techniques for Process Systems Engineering A Trust Region Framework for Heat Exchanger Network Synthesis with Detailed Individual Heat Exchanger Designs

Reference 19

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Observation c41c184a-9d56-4d93-b5f8-1a2f05de6705 · outbound

This paper cites Integrating Graph Neural Network-Based Surrogate Modeling with Inverse Design for Granular Flows.

Surrogate-Based Optimization Techniques for Process Systems Engineering Integrating Graph Neural Network-Based Surrogate Modeling with Inverse Design for Granular Flows

Reference 20

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Observation 313e49c2-937e-4f7f-92a4-af3d59478781 · outbound

This paper cites Algebraic Surrogate-Based Flexibility Analysis of Process Units with Complicating Process Constraints.

Surrogate-Based Optimization Techniques for Process Systems Engineering Algebraic Surrogate-Based Flexibility Analysis of Process Units with Complicating Process Constraints

Reference 21

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Observation 9a4515e3-2905-40f2-957b-3fc44e5e2ded · outbound

This paper cites A Quantile Neural Network Framework for Two-stage Stochastic Optimization.

Surrogate-Based Optimization Techniques for Process Systems Engineering A Quantile Neural Network Framework for Two-stage Stochastic Optimization

Reference 22

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Observation 2db885b8-7369-4cf1-8245-e2a9ac7c7dae · outbound

This paper cites Hybrid Semi-parametric Modeling in Separation Processes: A Review.

Surrogate-Based Optimization Techniques for Process Systems Engineering Hybrid Semi-parametric Modeling in Separation Processes: A Review

Reference 23

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Observation eb3fd48a-b9e6-4705-93dd-df1ff012a726 · outbound

This paper cites Data-Driven Models and Algorithms for Demand Response Scheduling of Air Separation Units.

Surrogate-Based Optimization Techniques for Process Systems Engineering Data-Driven Models and Algorithms for Demand Response Scheduling of Air Separation Units

Reference 24

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Observation 3a0add96-bcb8-4dab-b70a-693d1fb4f355 · outbound

This paper cites Data-Driven Optimization of Mixed- Integer Bi-Level Multi-Follower Integrated Planning and Scheduling Problems under Demand Uncertainty.

Surrogate-Based Optimization Techniques for Process Systems Engineering Data-Driven Optimization of Mixed- Integer Bi-Level Multi-Follower Integrated Planning and Scheduling Problems under Demand Uncertainty

Reference 25

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Observation 2d439953-d643-4405-8d2d-45350101b458 · outbound

This paper cites Data-Driven Construction of Convex Region Surrogate Models.

Surrogate-Based Optimization Techniques for Process Systems Engineering Data-Driven Construction of Convex Region Surrogate Models

Reference 26

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Observation aaef2391-3eee-4bfd-b245-a2793cb7be8c · outbound

This paper cites Data-Driven Strategies for Optimization of Integrated Chemical Plants.

Surrogate-Based Optimization Techniques for Process Systems Engineering Data-Driven Strategies for Optimization of Integrated Chemical Plants

Reference 27

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Observation aed10bd2-2371-4fb8-9aef-51b56915f7dc · outbound

This paper cites Hierarchical planning-scheduling-control -- Optimality surrogates and derivative-free optimization.

Surrogate-Based Optimization Techniques for Process Systems Engineering Hierarchical planning-scheduling-control -- Optimality surrogates and derivative-free optimization

Reference 28

Resolution: verified exact; local_arxiv, observed 2026-08-11T12:41:11.820782Z

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Observation 28b11b84-5247-4b03-947f-7e739cd37d1f · outbound

This paper cites High-Throughput Screening of Catalytically Active Inclusion Bodies Using Laboratory Automation and Bayesian Optimization.

Surrogate-Based Optimization Techniques for Process Systems Engineering High-Throughput Screening of Catalytically Active Inclusion Bodies Using Laboratory Automation and Bayesian Optimization

Reference 29

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Observation b7de77a3-8e50-412a-8101-f99c7b3c3eeb · outbound

This paper cites Into the Unknown: How Computation Can Help Explore Uncharted Material Space.

Surrogate-Based Optimization Techniques for Process Systems Engineering Into the Unknown: How Computation Can Help Explore Uncharted Material Space

Reference 30

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Observation 894e7a2e-edee-46de-a4a3-5e40c3ba32bd · outbound

This paper cites Multi-Objective Bayesian Optimisation Using q -Noisy Expected Hypervolume Improve- ment ( q NEHVI) for the Schotten–Baumann Reaction.

Surrogate-Based Optimization Techniques for Process Systems Engineering Multi-Objective Bayesian Optimisation Using q -Noisy Expected Hypervolume Improve- ment ( q NEHVI) for the Schotten–Baumann Reaction

Reference 31

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Observation 004a759f-5759-4f73-b157-523369f6209b · outbound

This paper cites Discrete and Mixed-Variable Experimental Design with Surrogate-Based Approach.

Surrogate-Based Optimization Techniques for Process Systems Engineering Discrete and Mixed-Variable Experimental Design with Surrogate-Based Approach

Reference 32

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Observation 16108bde-eefe-4032-8572-dd281f87fb4c · outbound

This paper cites Stochastic Data-Driven Model Predictive Control Using Gaussian Processes.

Surrogate-Based Optimization Techniques for Process Systems Engineering Stochastic Data-Driven Model Predictive Control Using Gaussian Processes

Reference 33

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Observation 071fa1c8-f4ec-431a-9fe7-379f3776868c · outbound

This paper cites Efficient Representation and Approximation of Model Predictive Control Laws via Deep Learning.

Surrogate-Based Optimization Techniques for Process Systems Engineering Efficient Representation and Approximation of Model Predictive Control Laws via Deep Learning

Reference 34

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Observation 85b324dc-b25d-463a-963a-2a5d9aca2fa7 · outbound

This paper cites A Data-driven Bayesian Approach for Optimal Dynamic Product Transitions.

Surrogate-Based Optimization Techniques for Process Systems Engineering A Data-driven Bayesian Approach for Optimal Dynamic Product Transitions

Reference 35

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Observation a9aa5e37-1d15-4949-9f78-2a73a79ed58a · outbound

This paper cites Online Feedback Optimization of Compressor Stations with Model Adaptation Using Gaussian Process Regression.

Surrogate-Based Optimization Techniques for Process Systems Engineering Online Feedback Optimization of Compressor Stations with Model Adaptation Using Gaussian Process Regression

Reference 36

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Observation 65155936-0501-4c82-a218-94150ede8ca8 · outbound

This paper cites A Data-driven Optimization Algorithm for Differential Algebraic Equations with Numerical Infeasibilities.

Surrogate-Based Optimization Techniques for Process Systems Engineering A Data-driven Optimization Algorithm for Differential Algebraic Equations with Numerical Infeasibilities

Reference 37

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Observation 4331ceaa-0e46-4914-bbb0-f19bf727b4bc · outbound

This paper cites Data-driven Decision-focused Surrogate Modeling.

Surrogate-Based Optimization Techniques for Process Systems Engineering Data-driven Decision-focused Surrogate Modeling

Reference 38

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Observation 738adada-67c6-4bf8-86ee-1eace0792e31 · outbound

This paper cites Global and Preference-based Optimization with Mixed Variables using Piecewise Affine Surrogates.

Surrogate-Based Optimization Techniques for Process Systems Engineering Global and Preference-based Optimization with Mixed Variables using Piecewise Affine Surrogates

Reference 39

Resolution: verified exact; local_arxiv, observed 2026-08-11T12:41:11.675359Z

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Observation 71f3e198-371a-4ab4-b931-5462d202efb4 · outbound

This paper cites Surrogate-Based Branch-and-Bound Algorithms for Simulation-Based Black-Box Optimization.

Surrogate-Based Optimization Techniques for Process Systems Engineering Surrogate-Based Branch-and-Bound Algorithms for Simulation-Based Black-Box Optimization

Reference 40

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Observation debafa65-aaf4-40f9-9386-70d033752008 · outbound

This paper cites Assuring Optimality in Surrogate-based Optimization: A Novel Theorem and Its Practical Implementation in Pressure Swing Adsorption Optimization.

Surrogate-Based Optimization Techniques for Process Systems Engineering Assuring Optimality in Surrogate-based Optimization: A Novel Theorem and Its Practical Implementation in Pressure Swing Adsorption Optimization

Reference 41

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Observation 1d7a9153-b2bf-4a9e-a29b-d8369a8f29bb · outbound

This paper cites A Bayesian Optimization Approach for Data-driven Mixed-integer Nonlinear Programming Problems.

Surrogate-Based Optimization Techniques for Process Systems Engineering A Bayesian Optimization Approach for Data-driven Mixed-integer Nonlinear Programming Problems

Reference 42

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Observation 02890a56-1494-479d-a88d-7f106f92ae27 · outbound

This paper cites Data-driven Coordination of Subproblems in Enterprise-wide Optimization under Organizational Considerations.

Surrogate-Based Optimization Techniques for Process Systems Engineering Data-driven Coordination of Subproblems in Enterprise-wide Optimization under Organizational Considerations

Reference 43

Resolution: verified exact; doi, observed 2026-08-11T12:41:10.146898Z

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Observation 3e2fcc79-c95d-421e-af78-7d65268ba791 · outbound

This paper cites Derivative-Free Optimization Methods.

Surrogate-Based Optimization Techniques for Process Systems Engineering Derivative-Free Optimization Methods

Reference 44

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Observation a4d419d7-ee00-4774-9f58-fb10f3f858f8 · outbound

This paper cites Derivative-free optimization methods.

Surrogate-Based Optimization Techniques for Process Systems Engineering Derivative-free optimization methods

Reference 45

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Observation 41245cc0-b5d0-4196-b1af-6e7511082cf9 · outbound

This paper cites Model-Based Derivative-Free Optimization Methods and Software.

Surrogate-Based Optimization Techniques for Process Systems Engineering Model-Based Derivative-Free Optimization Methods and Software

Reference 46

Resolution: unresolved; no resolver link, observed 2026-08-11T12:41:09.592269Z

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Observation db117555-55ab-452c-af56-5bfd054c33ab · outbound

This paper cites GPyOpt: A Bayesian Optimization Framework in Python.

Surrogate-Based Optimization Techniques for Process Systems Engineering GPyOpt: A Bayesian Optimization Framework in Python

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T12:41:13.036536Z

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Observation f0cbbdc5-37db-4ddd-b4d7-e8a91cad460f · outbound

This paper cites SOP: Parallel Surrogate Global Optimiza- tion with Pareto Center Selection for Computationally Expensive Single Objective Problems.

Surrogate-Based Optimization Techniques for Process Systems Engineering SOP: Parallel Surrogate Global Optimiza- tion with Pareto Center Selection for Computationally Expensive Single Objective Problems

Reference 48

Resolution: malformed identifier; no resolver link, observed 2026-08-11T12:41:09.602071Z

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Observation 1d198f1f-0bb3-417f-ad0b-a35c0a24807f · outbound

This paper cites A Stochastic Radial Basis Function Method for the Global Optimization of Expensive Functions.

Surrogate-Based Optimization Techniques for Process Systems Engineering A Stochastic Radial Basis Function Method for the Global Optimization of Expensive Functions

Reference 49

Resolution: verified exact; raw_fallback, observed 2026-08-11T12:41:11.625526Z

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Observation f6cf46cb-8447-4c80-bdf6-2b134ed3f85c · outbound

This paper cites ENTMOOT: A Framework for Optimization over Ensemble Tree Models.

Surrogate-Based Optimization Techniques for Process Systems Engineering ENTMOOT: A Framework for Optimization over Ensemble Tree Models

Reference 50

Resolution: malformed identifier; no resolver link, observed 2026-08-11T12:41:09.612712Z

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Observation 475f7b7b-aa3a-44b5-b23d-942284a4275c · outbound

This paper cites SNOBFIT – Stable Noisy Optimization by Branch and Fit.

Surrogate-Based Optimization Techniques for Process Systems Engineering SNOBFIT – Stable Noisy Optimization by Branch and Fit

Reference 51

Resolution: metadata mismatch; raw_fallback, observed 2026-08-11T12:41:11.411001Z

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Observation 4a1ed6fc-3cec-4327-b52d-45795f8095db · outbound

This paper cites ARGONAUT: AlgoRithms for Global Optimization of coNstrAined Grey-Box compUTational Problems.

Surrogate-Based Optimization Techniques for Process Systems Engineering ARGONAUT: AlgoRithms for Global Optimization of coNstrAined Grey-Box compUTational Problems

Reference 52

Resolution: unresolved; no resolver link, observed 2026-08-11T12:41:09.621684Z

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Observation b9a2446c-ef3a-4c69-9483-fb606fdebb7d · outbound

This paper cites Surrogate-based Superstructure Optimization Framework.

Surrogate-Based Optimization Techniques for Process Systems Engineering Surrogate-based Superstructure Optimization Framework

Reference 53

Resolution: verified exact; doi, observed 2026-08-11T12:41:10.087909Z

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Observation ee09362b-c371-4b81-9e30-e0eaf837709c · outbound

This paper cites Multi-Fidelity Data-Driven Design and Analysis of Reactor and Tube Simulations.

Surrogate-Based Optimization Techniques for Process Systems Engineering Multi-Fidelity Data-Driven Design and Analysis of Reactor and Tube Simulations

Reference 54

Resolution: malformed identifier; no resolver link, observed 2026-08-11T12:41:09.631039Z

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Observation 6e7d1d92-b646-413d-a1a1-b62eaa6698a3 · outbound

This paper cites An Algorithm for the Use of Surrogate Models in Modular Flowsheet Optimization.

Surrogate-Based Optimization Techniques for Process Systems Engineering An Algorithm for the Use of Surrogate Models in Modular Flowsheet Optimization

Reference 55

Resolution: verified exact; doi, observed 2026-08-11T12:41:10.454342Z

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Observation 6cc3a677-0087-43bb-87e6-8be1ae77d54a · outbound

This paper cites Real-Time Optimization Meets Bayesian Optimization and Derivative-Free Optimization: A Tale of Modifier Adaptation.

Surrogate-Based Optimization Techniques for Process Systems Engineering Real-Time Optimization Meets Bayesian Optimization and Derivative-Free Optimization: A Tale of Modifier Adaptation

Reference 56

Resolution: metadata mismatch; raw_fallback, observed 2026-08-11T12:41:11.234364Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation a445f096-f688-4315-964d-987886f19e4b · outbound

This paper cites A Radial Basis Function Method for Global Optimization.

Surrogate-Based Optimization Techniques for Process Systems Engineering A Radial Basis Function Method for Global Optimization

Reference 57

Resolution: verified exact; doi, observed 2026-08-11T12:41:10.067081Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 808224dc-dcc8-42de-96b7-3041bee60548 · outbound

This paper cites RBFOpt: An Open-Source Library for Black-Box Optimization with Costly Function Evaluations.

Surrogate-Based Optimization Techniques for Process Systems Engineering RBFOpt: An Open-Source Library for Black-Box Optimization with Costly Function Evaluations

Reference 58

Resolution: verified exact; doi, observed 2026-08-11T12:41:10.048360Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 87d72e1e-8840-4d0e-bbd0-efae4672e979 · outbound

This paper cites Global Convergence of General Derivative-Free Trust-Region Algorithms to First- and Second-Order Critical Points.

Surrogate-Based Optimization Techniques for Process Systems Engineering Global Convergence of General Derivative-Free Trust-Region Algorithms to First- and Second-Order Critical Points

Reference 59

Resolution: unresolved; no resolver link, observed 2026-08-11T12:41:09.654053Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7ce161e0-59d1-40a5-82ca-8bc0e7f1f8b4 · outbound

This paper cites A Progressive Barrier Derivative-Free Trust-Region Algorithm for Constrained Opti- mization.

Surrogate-Based Optimization Techniques for Process Systems Engineering A Progressive Barrier Derivative-Free Trust-Region Algorithm for Constrained Opti- mization

Reference 60

Resolution: unresolved; no resolver link, observed 2026-08-11T12:41:09.659014Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b1b2f7a4-1360-4dde-b413-81290f7539ea · outbound

This paper cites Bayesian Optimization with Inequality Constraints.

Surrogate-Based Optimization Techniques for Process Systems Engineering Bayesian Optimization with Inequality Constraints

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T12:41:13.010099Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 78ca1bd9-226b-44e7-882a-cc44b52739bf · outbound

This paper cites Derivative-free Optimization for Expensive Constrained Problems Using a Novel Expected Improvement Objective Function.

Surrogate-Based Optimization Techniques for Process Systems Engineering Derivative-free Optimization for Expensive Constrained Problems Using a Novel Expected Improvement Objective Function

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T12:41:12.985281Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 0f0fa86b-600c-4bf5-bdb8-536739a94b0e · outbound

This paper cites Advanced Trust Region Optimization Strategies for Glass Box/ Black Box Models.

Surrogate-Based Optimization Techniques for Process Systems Engineering Advanced Trust Region Optimization Strategies for Glass Box/ Black Box Models

Reference 63

Resolution: verified exact; doi, observed 2026-08-11T12:41:10.006505Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation ebdff3a8-077e-466a-b32d-716fbfcff160 · outbound

This paper cites Safe Real-Time Optimiza- tion Using Multi-Fidelity Gaussian Processes.

Surrogate-Based Optimization Techniques for Process Systems Engineering Safe Real-Time Optimiza- tion Using Multi-Fidelity Gaussian Processes

Reference 64

Resolution: metadata mismatch; raw_fallback, observed 2026-08-11T12:41:11.147144Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation c3686fd6-c84e-4eef-bc44-2383bdb04328 · outbound

This paper cites Dimensionality Reduction for Production Optimization Using Polynomial Approximations.

Surrogate-Based Optimization Techniques for Process Systems Engineering Dimensionality Reduction for Production Optimization Using Polynomial Approximations

Reference 65

Resolution: malformed identifier; raw_fallback, observed 2026-08-11T12:41:12.962066Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 79c98de6-eb67-4dbb-aafe-0fcd5ab169b1 · outbound

This paper cites Batch Bayesian Optimization via Local Penalization.

Surrogate-Based Optimization Techniques for Process Systems Engineering Batch Bayesian Optimization via Local Penalization

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T12:41:12.940969Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation de754598-53c2-4aa9-8485-4cc6c6ee0af2 · outbound

This paper cites Bayesian Optimization for Synthetic Gene Design.

Surrogate-Based Optimization Techniques for Process Systems Engineering Bayesian Optimization for Synthetic Gene Design

Reference 67

Resolution: unresolved; no resolver link, observed 2026-08-11T12:41:09.699711Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4e5f808e-ae7c-4b4b-839b-62dadc478776 · outbound

This paper cites GLASSES: Relieving The Myopia Of Bayesian Optimisation.

Surrogate-Based Optimization Techniques for Process Systems Engineering GLASSES: Relieving The Myopia Of Bayesian Optimisation

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T12:41:12.909821Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 106fc02a-969b-452c-aec2-bb678736422b · outbound

This paper cites Scalable Global Optimization via Local Bayesian Optimization.

Surrogate-Based Optimization Techniques for Process Systems Engineering Scalable Global Optimization via Local Bayesian Optimization

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T12:41:12.888360Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation c240cc67-64c9-4dcc-8ef2-0773fc7ad707 · outbound

This paper cites LightGBM: A Highly Efficient Gradient Boosting Decision Tree.

Surrogate-Based Optimization Techniques for Process Systems Engineering LightGBM: A Highly Efficient Gradient Boosting Decision Tree

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T12:41:12.861773Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 490eb672-35f0-417f-a7a3-9cdb553027b4 · outbound

This paper cites Classification And Regression Trees.

Surrogate-Based Optimization Techniques for Process Systems Engineering Classification And Regression Trees

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T12:41:12.838119Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 0527f264-976d-43a3-9575-6a02dae45220 · outbound

This paper cites Michael J. D. Powell. 29 July 1936—19 April 2015.

Surrogate-Based Optimization Techniques for Process Systems Engineering Michael J. D. Powell. 29 July 1936—19 April 2015

Reference 72

Resolution: malformed identifier; doi_truncated, observed 2026-08-11T12:41:11.022118Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 59eaeee9-8d56-49b2-85bd-3971c8293132 · outbound

This paper cites A Direct Search Optimization Method That Models the Objective and Constraint Functions by Linear Interpolation.

Surrogate-Based Optimization Techniques for Process Systems Engineering A Direct Search Optimization Method That Models the Objective and Constraint Functions by Linear Interpolation

Reference 73

Resolution: unresolved; no resolver link, observed 2026-08-11T12:41:09.737519Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e9fad459-d5ba-496a-9c91-d27156d6d2d5 · outbound

This paper cites CVXPY: A Python-Embedded Modeling Language for Convex Optimization.

Surrogate-Based Optimization Techniques for Process Systems Engineering CVXPY: A Python-Embedded Modeling Language for Convex Optimization

Reference 74

Resolution: unresolved; no resolver link, observed 2026-08-11T12:41:09.744791Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0c2b55a8-3ee9-4d87-b9df-a1cbe70becae · outbound

This paper cites High-Dimensional Derivative-Free Opti- mization via Trust Region Surrogates in Linear Subspaces.

Surrogate-Based Optimization Techniques for Process Systems Engineering High-Dimensional Derivative-Free Opti- mization via Trust Region Surrogates in Linear Subspaces

Reference 75

Resolution: verified exact; doi, observed 2026-08-11T12:41:09.966396Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation b45e9ed3-5141-4d0b-a9e4-30da1f773b63 · outbound

This paper cites Combining Radial Basis Function Surrogates and Dynamic Coordinate Search in High-Dimensional Expensive Black-Box Optimization.

Surrogate-Based Optimization Techniques for Process Systems Engineering Combining Radial Basis Function Surrogates and Dynamic Coordinate Search in High-Dimensional Expensive Black-Box Optimization

Reference 76

Resolution: unresolved; no resolver link, observed 2026-08-11T12:41:09.756435Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7ed97410-2f34-4f18-84a6-ffe933885649 · outbound

This paper cites A Radial Basis Function Method for Global Optimization.

Surrogate-Based Optimization Techniques for Process Systems Engineering A Radial Basis Function Method for Global Optimization

Reference 77

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T12:41:12.815305Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation b11a5298-9bf6-4eb4-81da-480bfbb0ba8b · outbound

This paper cites Parallel Stochastic Global Optimization Using Radial Basis Functions.

Surrogate-Based Optimization Techniques for Process Systems Engineering Parallel Stochastic Global Optimization Using Radial Basis Functions

Reference 78

Resolution: verified exact; raw_fallback, observed 2026-08-11T12:41:10.793780Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 2ca19155-3fa8-4496-b242-cd64d6ca49aa · outbound

This paper cites Dynamically Dimensioned Search Algorithm for Computationally Efficient Watershed Model Calibration.

Surrogate-Based Optimization Techniques for Process Systems Engineering Dynamically Dimensioned Search Algorithm for Computationally Efficient Watershed Model Calibration

Reference 79

Resolution: unresolved; no resolver link, observed 2026-08-11T12:41:09.773673Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation beec00b8-2933-4a48-860d-7983ac41be20 · outbound

This paper cites an unresolved cited work.

Surrogate-Based Optimization Techniques for Process Systems Engineering Unresolved cited work

Reference 80

Resolution: unresolved; no resolver link, observed 2026-08-11T12:41:09.778719Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9c018586-7c37-41b2-9151-3f7949acb168 · outbound

This paper cites Le mouvement brownien.

Surrogate-Based Optimization Techniques for Process Systems Engineering Le mouvement brownien

Reference 81

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T12:41:12.795716Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation acd8f160-2f16-47f0-8e73-ba0ecd4fa8e6 · outbound

This paper cites An Automatic Method for Finding the Greatest or Least Value of a Function.

Surrogate-Based Optimization Techniques for Process Systems Engineering An Automatic Method for Finding the Greatest or Least Value of a Function

Reference 82

Resolution: verified exact; doi, observed 2026-08-11T12:41:09.905020Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 1990f3f8-3fa4-4e94-aa87-c10eefdac5ae · outbound

This paper cites Random Optimization.

Surrogate-Based Optimization Techniques for Process Systems Engineering Random Optimization

Reference 83

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T12:41:12.751079Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 2a632e0e-0e88-40dd-b711-d66937316306 · outbound

This paper cites Assessing the Reliability of Different Real-time Optimization Methodologies.

Surrogate-Based Optimization Techniques for Process Systems Engineering Assessing the Reliability of Different Real-time Optimization Methodologies

Reference 84

Resolution: verified exact; doi, observed 2026-08-11T12:41:09.870817Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 1e39d333-31c4-4e19-bbb7-58155aa37ad4 · outbound

This paper cites an unresolved cited work.

Surrogate-Based Optimization Techniques for Process Systems Engineering Unresolved cited work

Reference 175

Resolution: parse uncertain; raw_fallback, observed 2026-08-11T12:41:12.772167Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 7789cf71-3641-4baf-9d4d-f18b3d40e554 · outbound

This paper cites (Visited on 07/01/2024).

Surrogate-Based Optimization Techniques for Process Systems Engineering (Visited on 07/01/2024)

Reference 2024

Resolution: verified exact; doi, observed 2026-08-11T12:41:10.282706Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

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