2000 character limit reached
Deep Learning Convective Flow Using Conditional Generative Adversarial Networks (2005.06422v2)
Published 13 May 2020 in physics.flu-dyn, cs.LG, and physics.comp-ph
Abstract: We developed a general deep learning framework, FluidGAN, capable of learning and predicting time-dependent convective flow coupled with energy transport. FluidGAN is thoroughly data-driven with high speed and accuracy and satisfies the physics of fluid without any prior knowledge of underlying fluid and energy transport physics. FluidGAN also learns the coupling between velocity, pressure, and temperature fields. Our framework helps understand deterministic multiphysics phenomena where the underlying physical model is complex or unknown.
- Rapp, B. E. “Microfluidics: Modeling, mechanics and mathematics”.
- “Discovering governing equations from data by sparse identification of nonlinear dynamical systems”. Proceedings of the National Academy of Sciences, 113(15), pp. 3932–3937.
- “The history began from alexnet: a comprehensive survey on deep learning approaches”. arXiv preprint arXiv:1803.01164.
- “Learning partial differential equations via data discovery and sparse optimization”. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences, 473(2197), p. 20160446.
- “Data-driven discovery of partial differential equations”. Science Advances, 3(4), p. e1602614.
- “Mechanics based tomography: a preliminary feasibility study”. Sensors, 17(5), p. 1075.
- “Physics informed deep learning (part ii): Data-driven discovery of nonlinear partial differential equations”. arXiv preprint arXiv:1711.10566.
- “Fishrecgan: An end to end gan based network for fisheye rectification and calibration”. arXiv preprint arXiv:2305.05222.
- “A transformer-based approach for translating natural language to bash commands”. In 2021 20th IEEE International Conference on Machine Learning and Applications (ICMLA), IEEE, pp. 1245–1248.
- “Reconstruction of three-dimensional porous media using generative adversarial neural networks”. Physical Review E, 96(4), Oct.
- Accelerating eulerian fluid simulation with convolutional networks.
- “Sketch2vis: Generating data visualizations from hand-drawn sketches with deep learning”. In 2021 20th IEEE International Conference on Machine Learning and Applications (ICMLA), IEEE, pp. 853–858.
- “tempogan”. ACM Transactions on Graphics, 37(4), Jul, p. 1–15.
- Deep learning the physics of transport phenomena.
- “Generative adversarial nets”. In Advances in neural information processing systems, pp. 2672–2680.
- “Unsupervised representation learning with deep convolutional generative adversarial networks”. arXiv preprint arXiv:1511.06434.
- “Image-to-image translation with conditional adversarial networks”. In Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 1125–1134.
- COMSOL Multiphysics. Comsol v5.3a.
- Batch normalization: Accelerating deep network training by reducing internal covariate shift.
Collections
Sign up for free to add this paper to one or more collections.
Paper Prompts
Sign up for free to create and run prompts on this paper using GPT-5.