Recorded talks

2025

1. [Penn State Mediaspace] Presenter: Kai Fukami (Tohoku University)

   • [Invited] K. Fukami,  ``Taming highly unsteady flows with data-oriented approaches: progress and outlook", in the Interdisciplinary Scientific Computing Laboratory (ISCL) Seminar Series at Pennsylvania State University, online, Jan 2025.

2024

1. [YouTube] Presenter: Kai Fukami (UCLA)

   • [Invited] K. Fukami, ``Taming extreme aerodynamic flows with generalized super resolution and manifold identification," in online webinar at the Laboratoire de Mécanique des Fluides de Lille (LFML), France, Feb 2024. [Flyer-A] [Flyer-B]

2023

1. [YouTube] Presenter: Kai Fukami (UCLA)

   • [Invited] K. Fukami, ``U.S. Ph.D. life as a Japanese Mechanical Engineer," in Seminar for the Japanese Graduate Student Association in the United States, online presentation, Dec 2023. [PDF]

2. [YouTube (23:15~)] Presenter: Kai Fukami (UCLA)

   • [Invited] K. Fukami, ``Developing artificial-intelligent techniques for turbulence," in Lightning Talks by the Amazon Fellows hosted by the UCLA-Amazon Science Hub for Humanity and Artificial Intelligence, Los Angeles, CA, USA, Feb 2023.

2022

1. [YouTube] Presenter: Kai Fukami (UCLA)

   • K. Fukami, K. Taira, ``Compact manifold representation of airfoil wake-vortex gust interaction," in 75th Annual Meeting of the APS Division of Fluid Dynamics, Indianapolis, Indiana, USA, Nov 2022.

2. [Google Drive] Presenter: Kai Fukami (UCLA)

   • K. Fukami, "Multi-layer perceptron-based identification and prediction for nonlinear systems," for the project study of ECE239AS Special Topics in Signals and Systems: Control, Identification, and Learning Algorithms in UCLA, May 2022. [Project Report]

3. [Conference link] Presenter: Kunihiko Taira (UCLA)

   • [Invited] K. Taira, C.-A. Yeh, K. Fukami,  ``Broadcasting perturbations over turbulence, in Causality in turbulence and transition, Madrid, Spain, May 2022. [PDF]

2021

1. [Slideslive] Presenter: Kazuto Hasegawa (Keio Univ.)

   • K. Hasegawa, K. Fukami, K. Fukagata, ``Visualization of nonlinear modal structures for three-dimensional unsteady fluid flows with customized decoder design," in Machine Learning and the Physical Sciences, Workshop at the 35th Conference on Neural Information Processing Systems (NeurIPS), Dec 2021

2. [Youtube] Presenter: Kai Fukami (UCLA)

   • K. Fukami, "Machine-learning-based reduced-order surrogate for sea surface temperature forecast," for the project study of C204 Introduction of Machine Learning for the Physical Sciences in UCLA, Dec 2021. [Project Report]

3. [Youtube] Presenter: Kai Fukami (UCLA)

   • K. Fukami, K. Taira, ``Robust machine learning of turbulence through generalized Buckingham Pi-inspired pre-processing of training data," in 74th Annual Meeting of the APS Division of Fluid Dynamics, Phoenix, USA, Nov 2021. 

4. [Google Drive] Presenter: Taichi Nakamura (Keio Univ.)

• [Invited] T. Nakamura, K. Fukami, K. Hasegawa, Y. Nabae, K. Fukagata, "Utilization of autoencoder-based nonlinear manifolds for fluid flow forecasts driven with long short-term memory," in DataLearning workshop of Data Science Institute, Imperial College London, online, Mar 2021.

5. [Google Drive] Presenter: Kai Fukami (UCLA)

• [Invited] K. Fukami, R. Maulik, N. Ramachandra, K. Fukagata, K. Taira, "Toward practical global field reconstruction from sparse sensors with deep learning," in DataLearning workshop of Data Science Institute, Imperial College London, online, Mar 2021.

6. [Slideslive] Presenter: Kai Fukami (UCLA)

• K. Fukami, T. Murata, K. Fukagata, “Low-dimensionalized flow representation with customized autoencoders,” in 14th World Congress on Computational Mechanics (WCCM) ECCOMAS Congress 2020, online presentation, Jan 2021.

7. [Slideslive] Presenter: Masaki Morimoto (Keio Univ.)

• M. Morimoto, K. Fukami, H. Murakami, K. Fukagata, “The use of convolutional neural networks for PIV data augmentation,” in 14th World Congress on Computational Mechanics (WCCM) ECCOMAS Congress 2020, online presentation, Jan 2021.

2020

1. [Youtube] Presenter: Taichi Nakamura (Keio Univ.)

• T. Nakamura, K. Fukami, K. Hasegawa, Y. Nabae, K. Fukagata, "CNN-AE/LSTM based turbulent flow forecast on low-dimensional latent space," in Machine Learning and the Physical Sciences, Workshop at the 34th Conference on Neural Information Processing Systems (NeurIPS), online, Dec 2020.

2. [Youtube] Presenter: Kai Fukami (UCLA)

• K. Fukami, R. Maulik, N. Ramachandra, K. Fukagata, K. Taira, "Probabilistic neural network-based reduced-order surrogate for fluid flows," in Machine Learning and the Physical Sciences, Workshop at the 34th Conference on Neural Information Processing Systems (NeurIPS), online, Dec 2020.

3. [Youtube] Presenter: Kai Fukami (UCLA)

• K. Fukami, R. Maulik, N. Ramachandra, K. Taira, K. Fukagata, “Unstructured fluid flow data recovery using machine learning and Voronoi diagrams", in 73rd Annual Meeting of the APS Division of Fluid Dynamics, online presentation, Nov 2020.

4. [Box] Presenter: Masaki Morimoto (Keio Univ.)

• M. Morimoto, K. Fukami, K. Fukagata, "Visualization for internal procedure of neural networks for fluid flows", in 73rd Annual Meeting of the APS Division of Fluid Dynamics, online presentation, Nov 2020

5. [Box] Presenter: Naoki Moriya (Keio Univ.)

• N. Moriya, K. Fukami, Y. Nabae, M. Morimoto, T. Nakamura, K. Fukagata, "Convolutional neural network based wall modeling for large eddy simulation in a turbulent channel flow", in 73rd Annual Meeting of the APS Division of Fluid Dynamics, online presentation, Nov 2020