A full order, reduced order and machine learning model pipeline for efficient prediction of reactive flows

DOI10.1007/978-3-030-97549-4_43zbMath1501.76062arXiv2104.02800OpenAlexW3146713108MaRDI QIDQ2128470

Maha Youssef, Pavel Gavrilenko, Bernard Haasdonk, Mario Ohlberger, O. P. Iliev, Tizian Wenzel, Felix Schindler, Pavel Toktaliev

Publication date: 22 April 2022

Full work available at URL: https://arxiv.org/abs/2104.02800

zbMATH Keywords

kernel methodreduced-order modelchemical conversion ratedata based machine learningprojection-based reduced basis model

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Reaction effects in flows (76V05) Basic methods in fluid mechanics (76M99)

Related Items (1)

A New Certified Hierarchical and Adaptive RB-ML-ROM Surrogate Model for Parametrized PDEs

Uses Software

• pyMOR
• hapod

Cites Work

• Unnamed Item
• Decision tree induction based on efficient tree restructuring
• A novel class of stabilized greedy kernel approximation algorithms: convergence, stability and uniform point distribution
• pyMOR -- Generic Algorithms and Interfaces for Model Order Reduction
• 9 Kernel methods for surrogate modeling
• Model Reduction and Approximation
• Hierarchical Approximate Proper Orthogonal Decomposition
• Scattered Data Approximation

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