Data-Driven Spatial Branch-And-Bound Algorithms For Black-Box Optimization

Zhai, Jianyuan; Boukouvala, Fani

doi:10.1016/b978-0-12-818597-1.50012-6

Cited by 4 publications

(2 citation statements)

References 21 publications

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“…From a computational perspective, data-driven approaches may also be used to reduce computation load and speed up the enumeration process [101] in scenarios where computing resource is limited on the application-side or real-time performance (e.g., interaction) is needed. Machine learning methods, for example, may be used to heuristically prune low-potential enumeration spaces or conversely recommend a list of high-potential regions for the search to continue [13,197]. Other settings such as adversarial and reinforcement learning can also be helpful in this scenario.…”

Section: Synergistic Integration With Advances In Related Fieldsmentioning

confidence: 99%

Statistically-Robust Clustering Techniques for Mapping Spatial Hotspots: A Survey

Xie,

Shekhar,

2021

Preprint

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Mapping of spatial hotspots, i.e., regions with significantly higher rates or probability density of generating certain events (e.g., disease or crime cases), is a important task in diverse societal domains, including public health, public safety, transportation, agriculture, environmental science, etc. Clustering techniques required by these domains differ from traditional clustering methods due to the high economic and social costs of spurious results (e.g., false alarms of crime clusters). As a result, statistical rigor is needed explicitly to control the rate of spurious detections. To address this challenge, techniques for statistically-robust clustering have been extensively studied by the data mining and statistics communities. In this survey we present an up-to-date and detailed review of the models and algorithms developed by this field. We first present a general taxonomy of the clustering process with statistical rigor, covering key steps of data and statistical modeling, region enumeration and maximization, significance testing, and data update. We further discuss different paradigms and methods within each of key steps. Finally, we highlight research gaps and potential future directions, which may serve as a stepping stone in generating new ideas and thoughts in this growing field and beyond.

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Section: Synergistic Integration With Advances In Related Fieldsmentioning

confidence: 99%

Statistically-Robust Clustering Techniques for Mapping Spatial Hotspots: A Survey

Xie,

Shekhar,

2021

Preprint

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“…This may be due to limitations in the microkinetic model used, such as neglecting adsorbate-adsorbate interactions or possible missing reactions such as oxygen transfer to the bulk. Addressing these challenges will require further development to the TAPsolver framework, such as integration with global optimization routines [87], inclusion of additional elementary steps and lateral adsorbate interactions, or application of model reduction criteria [88,89]. Nonetheless, this brief case study illustrates TAPsolver's ability to fit even noisy data sets, and highlights the ability to directly extract intrinsic kinetic parameters from TAP data.…”

Section: Application: Analyzing Experimental Carbon Monoxide Oxidatio...mentioning

confidence: 99%

TAPsolver: A Python package for the simulation and analysis of TAP reactor experiments

Yonge¹,

Kunz²,

Batchu³

et al. 2020

Preprint

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An open-source, Python-based Temporal Analysis of Products (TAP) reactor simulation and processing program is introduced. TAPsolver utilizes algorithmic differentiation for the calculation of highly accurate derivatives, which are used to perform sensitivity analyses and PDE-constrained optimization. The tool supports constraints to ensure thermodynamic consistency, which can lead to more accurate parameters and assist in mechanism discrimination. The mathematical and structural details of TAPsolver are outlined, as well as validation of the forward and inverse problems against well-studied prototype problems. Benchmarks of the code are presented, and a case study for extracting thermodynamically-consistent kinetic parameters from experimental TAP measurements of CO oxidation on supported platinum particles is presented. TAPsolver will act as a foundation for future development and dissemination of TAP data processing techniques.

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TAPsolver: A Python package for the simulation and analysis of TAP reactor experiments

Yonge

Kunz

Batchu

et al. 2021

Chemical Engineering Journal

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Data-Driven Spatial Branch-And-Bound Algorithms For Black-Box Optimization

Cited by 4 publications

References 21 publications

Statistically-Robust Clustering Techniques for Mapping Spatial Hotspots: A Survey

Statistically-Robust Clustering Techniques for Mapping Spatial Hotspots: A Survey

TAPsolver: A Python package for the simulation and analysis of TAP reactor experiments

TAPsolver: A Python package for the simulation and analysis of TAP reactor experiments

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