CONVOLUTIONAL NEURAL NETWORKS FOR PROBLEMS IN TRANSPORT PHENOMENA: A THEORETICAL MINIMUM Article Swipe
Arjun Bhasin
,
Aashutosh Mistry
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1615/jflowvisimageproc.2022043908
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1615/jflowvisimageproc.2022043908
Convolutional neural network (CNN), a deep learning algorithm, has gained popularity in technological applications that rely on interpreting images (typically, an image is a 2D field of pixels). Transport phenomena is the science of studying different fields representing mass, momentum, or heat transfer. Some of the common fields are species concentration, fluid velocity, pressure, and temperature. Each of these fields can be expressed as an image(s). Consequently, CNNs can be leveraged to solve specific scientific problems in transport phenomena. Herein, we show that such problems can be grouped into three basic categories: (a) mapping a field to a descriptor (b) mapping a field to another field, and (c) mapping a descriptor to a field. After reviewing the representative transport phenomena literature for each of these categories, we illustrate the necessary steps for constructing appropriate CNN solutions using sessile liquid drops as an exemplar problem. If sufficient training data is available, CNNs can considerably speed up the solution of the corresponding problems. The present discussion is meant to be minimalistic such that readers can easily identify the transport phenomena problems where CNNs can be useful as well as construct and/or assess such solutions.
Related Topics
Concepts
Convolutional neural network
Field (mathematics)
Computer science
Construct (python library)
Artificial intelligence
Popularity
Pixel
Deep learning
Transfer of learning
Transport phenomena
Image (mathematics)
Algorithm
Mathematics
Physics
Mechanics
Social psychology
Programming language
Pure mathematics
Psychology
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1615/jflowvisimageproc.2022043908
- OA Status
- green
- Cited By
- 3
- References
- 105
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4312677814
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4312677814Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1615/jflowvisimageproc.2022043908Digital Object Identifier
- Title
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CONVOLUTIONAL NEURAL NETWORKS FOR PROBLEMS IN TRANSPORT PHENOMENA: A THEORETICAL MINIMUMWork title
- Type
-
articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2022Year of publication
- Publication date
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2022-11-17Full publication date if available
- Authors
-
Arjun Bhasin, Aashutosh MistryList of authors in order
- Landing page
-
https://doi.org/10.1615/jflowvisimageproc.2022043908Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
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greenOpen access status per OpenAlex
- OA URL
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https://www.osti.gov/biblio/1989939Direct OA link when available
- Concepts
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Convolutional neural network, Field (mathematics), Computer science, Construct (python library), Artificial intelligence, Popularity, Pixel, Deep learning, Transfer of learning, Transport phenomena, Image (mathematics), Algorithm, Mathematics, Physics, Mechanics, Social psychology, Programming language, Pure mathematics, PsychologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
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3Total citation count in OpenAlex
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2025: 1, 2023: 2Per-year citation counts (last 5 years)
- References (count)
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105Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| referenced_works | https://openalex.org/W2271840356, https://openalex.org/W2748902594, https://openalex.org/W2797406032, https://openalex.org/W2999555012, https://openalex.org/W2969577977, https://openalex.org/W3175301090, https://openalex.org/W2981246174, https://openalex.org/W3047006557, https://openalex.org/W2973119841, https://openalex.org/W3035733053, https://openalex.org/W4220717841, https://openalex.org/W2917818392, https://openalex.org/W3014753739, https://openalex.org/W3157599415, https://openalex.org/W3161200675, https://openalex.org/W764651262, https://openalex.org/W4221011222, https://openalex.org/W3125013357, https://openalex.org/W2129159835, https://openalex.org/W3046342431, https://openalex.org/W3001544254, https://openalex.org/W3208290324, https://openalex.org/W3035246486, https://openalex.org/W2101926813, https://openalex.org/W3097124738, https://openalex.org/W2967095864, https://openalex.org/W3037979004, https://openalex.org/W2962949934, https://openalex.org/W2998036231, https://openalex.org/W2515505748, https://openalex.org/W1565402342, https://openalex.org/W2965300934, https://openalex.org/W2194775991, https://openalex.org/W2953078715, https://openalex.org/W3003618922, https://openalex.org/W2100495367, https://openalex.org/W3048299477, https://openalex.org/W4221129496, https://openalex.org/W2574952845, https://openalex.org/W1982082993, https://openalex.org/W3151042244, https://openalex.org/W3165730810, https://openalex.org/W2964121744, https://openalex.org/W2618530766, https://openalex.org/W3116287554, https://openalex.org/W2585298970, https://openalex.org/W2144354855, https://openalex.org/W576767351, https://openalex.org/W2147800946, https://openalex.org/W2112796928, https://openalex.org/W4211174697, https://openalex.org/W3168997536, https://openalex.org/W2534240011, https://openalex.org/W3005641041, https://openalex.org/W3109972557, https://openalex.org/W3139111283, https://openalex.org/W4229052372, https://openalex.org/W3048716856, https://openalex.org/W3136441139, https://openalex.org/W2995823297, https://openalex.org/W2802191815, https://openalex.org/W3086398327, https://openalex.org/W2783953992, https://openalex.org/W3023125761, https://openalex.org/W3205762190, https://openalex.org/W4298392474, https://openalex.org/W3119946971, https://openalex.org/W3209556830, https://openalex.org/W6630447605, https://openalex.org/W3124389259, https://openalex.org/W6910563834, https://openalex.org/W1980196517, https://openalex.org/W3203648016, https://openalex.org/W3163000876, https://openalex.org/W3108318745, https://openalex.org/W2604252804, https://openalex.org/W2899283552, https://openalex.org/W2745110207, https://openalex.org/W3212115482, https://openalex.org/W4287813931, https://openalex.org/W2592720772, https://openalex.org/W4287550688, https://openalex.org/W1965368138, https://openalex.org/W3211126885, https://openalex.org/W2962835968, https://openalex.org/W4210837481, https://openalex.org/W3135457600, https://openalex.org/W3092231855, https://openalex.org/W2097117768, https://openalex.org/W3165475162, https://openalex.org/W2963391479, https://openalex.org/W3112314327, https://openalex.org/W2996132844, https://openalex.org/W3136245336, https://openalex.org/W3011193865, https://openalex.org/W3164221504, https://openalex.org/W4296270275, https://openalex.org/W3210848962, https://openalex.org/W3099117237, https://openalex.org/W3131838898, https://openalex.org/W3201195150, https://openalex.org/W3099412592, https://openalex.org/W3084290329, https://openalex.org/W3161295900, https://openalex.org/W3106400375 |
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