Convolutional neural networks for steady flow approximation

Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

Volumn 13-17-August-2016, Issue , 2016, Pages 481-490

  Guo, Xiaoxiao ^a   Li, Wei ^b   Iorio, Francesco ^b  

^a UNIVERSITY OF MICHIGAN   (United States)

^b AUTODESK RESEARCH   (United States)

Author keywords

Computational fluid dynamics; Convolutional neural networks; Machine learning; Surrogate models

Indexed keywords

AERODYNAMICS; APPROXIMATION ALGORITHMS; ARTIFICIAL INTELLIGENCE; CONVOLUTION; DATA MINING; DESIGN; FLUID DYNAMICS; ITERATIVE METHODS; LAMINAR FLOW; LEARNING SYSTEMS; NETWORK ARCHITECTURE; NEURAL NETWORKS; OPTIMIZATION; PERSONNEL TRAINING; SYSTEMS ANALYSIS; VELOCITY;

CONVOLUTIONAL NEURAL NETWORK; DESIGN SPACE EXPLORATION; EFFICIENT ESTIMATION; FLEXIBLE APPROXIMATION MODEL; GEOMETRY REPRESENTATION; OPTIMIZATION PROBLEMS; REAL-TIME PREDICTION; SURROGATE MODEL;

COMPUTATIONAL FLUID DYNAMICS;

EID: 84984994117     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/2939672.2939738     Document Type: Conference Paper

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