Neural Network-Based Projective Grid Model for Learning Representation of Surface and Wave Foams
In this paper, we propose a projective grid model that enables learning representation of foam effects using artificial neural networks. In 3D fluid simulations, foam is one of the most representative secondary effects in water. Consequently, the processes of foam generation, advection, and dissolut...
Saved in:
| Main Author: | |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/11097292/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1839605332826914816 |
|---|---|
| author | Jong-Hyun Kim |
| author_facet | Jong-Hyun Kim |
| author_sort | Jong-Hyun Kim |
| collection | DOAJ |
| description | In this paper, we propose a projective grid model that enables learning representation of foam effects using artificial neural networks. In 3D fluid simulations, foam is one of the most representative secondary effects in water. Consequently, the processes of foam generation, advection, and dissolution are computationally expensive. However, no prior research has explored representing these processes using neural networks. This paper introduces a constitutive model for learning foam effects in projective space with neural networks. Learning 3D fluid simulation is a complex and multi-faceted challenge, but our approach simplifies the design of foam effects by leveraging a 2D projective space rather than a fully 3D space. The proposed projective grid model consists of the following components: 1) learning the conditions under which foam particles are generated in the projective shape, 2) distinguishing between surface foam and wave foam based on varying conditions instead of relying on a single foam texture, 3) learning the advection process of different foam types, and 4) learning the dissolution process through representation learning. As a result, our method enables efficient representation of 3D foam effects without the need for complex numerical calculations, demonstrating its effectiveness across various scenarios. |
| format | Article |
| id | doaj-art-92b0a28f3e614e43b71805642aee7c7e |
| institution | Matheson Library |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-92b0a28f3e614e43b71805642aee7c7e2025-08-01T23:01:25ZengIEEEIEEE Access2169-35362025-01-011313363513364910.1109/ACCESS.2025.359312011097292Neural Network-Based Projective Grid Model for Learning Representation of Surface and Wave FoamsJong-Hyun Kim0https://orcid.org/0000-0003-1603-2675Department of Artificial Intelligence, Design Technology, Graduate School of Electrical and Computer Engineering, College of Software and Convergence, Inha University, Michuhol-gu, Incheon, South KoreaIn this paper, we propose a projective grid model that enables learning representation of foam effects using artificial neural networks. In 3D fluid simulations, foam is one of the most representative secondary effects in water. Consequently, the processes of foam generation, advection, and dissolution are computationally expensive. However, no prior research has explored representing these processes using neural networks. This paper introduces a constitutive model for learning foam effects in projective space with neural networks. Learning 3D fluid simulation is a complex and multi-faceted challenge, but our approach simplifies the design of foam effects by leveraging a 2D projective space rather than a fully 3D space. The proposed projective grid model consists of the following components: 1) learning the conditions under which foam particles are generated in the projective shape, 2) distinguishing between surface foam and wave foam based on varying conditions instead of relying on a single foam texture, 3) learning the advection process of different foam types, and 4) learning the dissolution process through representation learning. As a result, our method enables efficient representation of 3D foam effects without the need for complex numerical calculations, demonstrating its effectiveness across various scenarios.https://ieeexplore.ieee.org/document/11097292/Fluid simulationsfoam effectsartificial neural networksprojective gridprojection spacesurface foam |
| spellingShingle | Jong-Hyun Kim Neural Network-Based Projective Grid Model for Learning Representation of Surface and Wave Foams IEEE Access Fluid simulations foam effects artificial neural networks projective grid projection space surface foam |
| title | Neural Network-Based Projective Grid Model for Learning Representation of Surface and Wave Foams |
| title_full | Neural Network-Based Projective Grid Model for Learning Representation of Surface and Wave Foams |
| title_fullStr | Neural Network-Based Projective Grid Model for Learning Representation of Surface and Wave Foams |
| title_full_unstemmed | Neural Network-Based Projective Grid Model for Learning Representation of Surface and Wave Foams |
| title_short | Neural Network-Based Projective Grid Model for Learning Representation of Surface and Wave Foams |
| title_sort | neural network based projective grid model for learning representation of surface and wave foams |
| topic | Fluid simulations foam effects artificial neural networks projective grid projection space surface foam |
| url | https://ieeexplore.ieee.org/document/11097292/ |
| work_keys_str_mv | AT jonghyunkim neuralnetworkbasedprojectivegridmodelforlearningrepresentationofsurfaceandwavefoams |