Conference Talks
Conference Talks
H. Odaka, K. Fukami, K. Taira, “Plunging airfoil wakes in low-order latent space coordinates,” in AIAA Aviation Forum 2025, Las Vegas, Nevada, USA, July 2025.
[Invited] K. Fukami, “Observable-augmented manifold learning for unsteady flow analysis,” in the IUTAM Symposium on Machine Learning in Diverse Fluid Mechanics, Okinawa, Japan, May 2025.
[Invited (Keynote)] K. Fukami, “Data-oriented analysis of extremely unsteady flows,” in the RIKEN next-generation CAE consortium for combustion systems, Hyogo, Japan, Apr 2025. [PDF] [Picture]
[Invited] K. Fukami, “Data-oriented approaches for analysis of unsteady flows,” in a seminar on “Fluids and Informatics” at the Japan Society of Mechanical Engineers, online, Apr 2025.
K. Taira, K. Fukami, L. R. Smith, Y. Zhong, A. J. Linot, H. Odaka, B. Lopez-Doriga, “Data-driven analysis, modeling, and control of extreme aerodynamic flows,” in the EuroMech Colloquium on Data-Driven Fluid Dynamics and the 2nd ERCOFTAC Workshop on Machine Learning for Fluid Dynamics, London, UK, Apr 2025.
[Invited] K. Fukami, “Identifying interpolatory and extrapolatory vortical structures of data-driven fluid dynamics,” in 3rd Workshop on Data-Driven Fluid Dynamics, Nagoya, Japan, Mar 2025.
K. Fukami, “Data-driven analysis of highly unsteady flows: progress and outlook,” in the seminar at Spacecraft Thermal and Fluids Systems Laboratory, Tohoku University, Sendai, Japan, Feb 2025.
[Invited] K. Fukami, “Extreme Aerodynamic Manifold: Data-Driven Modeling and Control of Highly Gusty Flows,” in the 18th CCMR symposium at Toyo University, Tokyo, Japan, Feb 2025.
[Invited] K. Fukami, “Generalized Super-Resolution Analysis with Machine Learning of Turbulence,” in the 1st Workshop for Digital Twin and AI-Integrated Design for Mechanical Systems, Incheon, Korea, Feb 2025. [Poster]
[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. [Recorded Video]
K. Fukami, H. Nakao, K. Taira, “Quick mitigation of extreme-gust effects with phase-amplitude modeling on a low-dimensional manifold,” in the 38th CFD symposium, Tokyo, Japan, Dec 2024.
K. Fukami, K. Taira, “Single-snapshot machine learning for super-resolution analysis of turbulence,” in 77th Annual Meeting of the APS Division of Fluid Dynamics, Salt Lake City, Utah, USA, Nov 2024.
J. Tran, K. Fukami, K. Inada, D. Umehara, Y. Ono, K. Ogawa, K. Taira, “Data-driven automotive aerodynamic shape optimization,” in 77th Annual Meeting of the APS Division of Fluid Dynamics, Salt Lake City, Utah, USA, Nov 2024.
J. Tran, K. Fukami, K. Inada, D. Umehara, Y. Ono, K. Ogawa, K. Taira, “Data-driven vehicle design optimization through aerodynamics informed dimensionality reduction,” in SIAM Conference on Mathematics of Data Science (MDS24), Atlanta, Georgia, USA, Oct 2024.
H. Odaka, K. Fukami, K. Taira, “Latent space representation of plunging airfoil wakes using a drag-augmented autoencoder,” in 1st European Fluid Dynamics Conference (EFDC1), Aachen, Germany, Sep 2024.
K. Fukami, H. Nakao, K. Taira, “Data-driven lift regulation of extreme vortex-airfoil interactions,” in ICTAM 2024, Daegu, South Korea, Aug 2024.
K. Fukami, K. Taira, “Data-driven modeling, sensing, and control of extreme vortex-airfoil interactions,” in AIAA Aviation Forum 2024, Las Vegas, Nevada, USA, July-Aug 2024.
K. Fukami, K. Taira, “Extreme aerodynamics of vortex impingement: Machine-learning-based compression and situational awareness,” 13th International Symposium on Turbulence and Shear Flow Phenomena (TSFP13), Montréal, Canada, June 2024.
[Invited] K. Taira, K. Fukami, “Super-Resolution Analysis: Revisiting the Training Process/Data for Machine Learning in Fluid Dynamics,” in Advancing fluid and soft-matter dynamics with machine learning and data science: a conference at UW-Madison, Madison, Wisconsin, USA, June 2024.
[Invited] K. Fukami, “Let us machine-learn fluid dynamics!,” in 73rd SCJSF & JABA Forum, Los Angeles, California, USA, May 2024. [PDF]
L. R. Smith, K. Fukami, G. Sedky, A. R. Jones, K. Taira, “Discrete gust encounters through the lens of persistent homology,” in 3rd Colloquium on Vortex Dominated Flows (DisCoVor), Delft, The Netherlands, Apr 2024.
J. Tran, K. Fukami, K. Taira, “Aerodynamics-informed manifold learning for data-driven design optimization of automobiles,” in 17th Southern California Flow Physics Symposium (SoCal Fluids XVII), Irvine, California, USA, Apr 2024. [PDF]
K. Fukami, H. Nakao, K. Taira, “Phase-amplitude model-based control of extreme vortex-airfoil interactions on a low-dimensional manifold,” in 17th Southern California Flow Physics Symposium (SoCal Fluids XVII), Irvine, California, USA, Apr 2024. [PDF]
[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. [YouTube] [Flyer-A] [Flyer-B]
[Invited] K. Fukami, K. Taira, “Extreme Aerodynamic Manifold: Vortex-Airfoil Interactions,” in Remote Colloquium on Vortex Dominated Flows (ReCoVor), online presentation, Jan 2024.
[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. [YouTube] [PDF]
K. Fukami, K. Taira, “Extreme Aerodynamic Manifold: Vortex-Airfoil Interactions,” in 76th Annual Meeting of the APS Division of Fluid Dynamics, Washington, DC, USA, Nov 2023.
H. Odaka, K. Fukami, K. Taira, “Data-driven compression of plunging airfoil wakes,” in 76th Annual Meeting of the APS Division of Fluid Dynamics, Washington, DC, USA, Nov 2023.
L. R. Smith, K. Fukami, G. Sedky, A. R. Jones, K. Taira, “Analyzing the Dynamics of Discrete Gust Encounters with Persistent Homology,” in 76th Annual Meeting of the APS Division of Fluid Dynamics, Washington, DC, USA, Nov 2023.
[Invited] K. Fukami, K. Taira, “Grasping extreme aerodynamics on a low-dimensional manifold,” in Science Hub Showcase 2023 hosted by the UCLA-Amazon Science Hub for Humanity and Artificial Intelligence, Los Angeles, CA, USA, Oct 2023.
K. Fukami, “Let us machine-learn fluid dynamics: A perspective of global field reconstruction and nonlinear manifold identification,” Aerodynamic Design Research Group at Tohoku University, Miyagi, Japan, Aug 2023.
K. Fukami, R. Maulik, N. Ramachandra, K. Fukagata, K. Taira, “Global field reconstruction from sparse sensors with Voronoi tessellation-assisted deep learning,” Structures-Computer Interaction Lab at UCLA, California, USA, Jun 2023.
L. R. Smith, K. Fukami, G. Sedky, A. R. Jones, K. Taira, “A topological approach to modeling the dynamics of unsteady gust encounters,” in 16th Southern California Flow Physics Symposium (SoCal Fluids XVI), San Diego, California, USA, Apr 2023.
H. Odaka, K. Fukami, K. Taira, “Feature extraction from plunging airfoil wakes using an autoencoder,” in 16th Southern California Flow Physics Symposium (SoCal Fluids XVI), San Diego, California, USA, Apr 2023.
K. Fukami, K. Taira, “Discovering the nonlinear manifold of extreme aerodynamic flows,” in 16th Southern California Flow Physics Symposium (SoCal Fluids XVI), San Diego, California, USA, Apr 2023.
[Invited] K. Fukami, K. Fukagata, K. Taira, “Super-resolving turbulent flows with machine learning: a survey,” in SIAM Conference on Computational Science and Engineering (CSE23), Amsterdam, The Netherlands, Feb 2023
[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. [PDF]
[Invited] K. Fukami, K. Taira, “Machine learning for fluid dynamics -- Part III: Applications,” at Honda Motor Co.,LTD., Tochigi, Japan, Dec 2022.
[Invited] K. Taira, K. Fukami, “Machine learning for fluid dynamics -- Part II: Supervised learning,” at Honda Motor Co.,LTD., Tochigi, Japan, Dec 2022.
[Invited] K. Taira, K. Fukami, “Machine learning for fluid dynamics -- Part I: Unsupervised learning,” at Honda Motor Co.,LTD., Tochigi, Japan, Dec 2022.
K. Fukami, K. Taira, “Learning the nonlinear manifold of extreme aerodynamics,” in Machine Learning and the Physical Sciences, Workshop at the 36th Conference on Neural Information Processing Systems (NeurIPS), Dec 2022.
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.
V. Anantharaman, K. Fukami, K. Taira, “Image and video compression of fluid flow data,” in 75th Annual Meeting of the APS Division of Fluid Dynamics, Indianapolis, Indiana, USA, Nov 2022.
[Invited] K. Fukami, K. Taira, “Finding scale-invariant turbulent flow structures for enhanced machine learning,” in SIAM Conference on Mathematics of Data Science (MDS22), San Diego, California, USA, Sep 2022.
[Invited] K. Fukami, “Physics-inspired machine learning for fluid flow reconstruction and reduced-complexity modeling,” in the seminar at Osaka University, Osaka, Japan, Sep 2022.
K. Fukami, “Towards phase-inspired airfoil wake control in autoencoder latent space,” in the seminar at Tokyo Institute of Technology, Tokyo, Japan, Sep 2022.
[Invited] K. Fukami, “Reconstructing and modeling unsteady flows with physics-inspired machine learning,” in 2nd US-Japan Workshop on Data-Driven Fluid Dynamics, Kobe, Japan, Sep 2022.
[Invited] R. Maulik, K. Fukami, M. Morimoto, N. Ramachandra, R. Vinuesa, K. Fukagata, K. Taira, “Quantifying uncertainty in deep learning for fluid flow reconstruction,” in USACM Thematic Conference on Uncertainty Quantification for Machine Learning Integrated Physics Modeling (MLIP), Arlington, Virginia, USA, Aug 2022. [PDF]
Y. Zhong, K. Fukami, B. An, K. Taira, “Machine-learning-based reconstruction of transient vortex-airfoil wake interaction,” AIAA Aviation Forum 2022, Chicago, Illinois, Jun 2022. [Paper]
K. Fukami, V. Godavarthi, Y. Zhong, C.-A. Yeh, K. Taira, “Time-varying broadcast mode analysis for airfoil wake dynamics,” in IUTAM Symposium on Data-driven modeling and optimization in fluid mechanics, Aarhus, Denmark, Jun 2022.
K. Fukami, B. An, M. Nohmi, M. Obuchi, K. Taira, “Machine-learning-based turbulent state estimation from pressure sensors in a pump sump,” in the 86th conference of turbomachinery society of Japan, online, May 2022.
[Invited] K. Taira, C.-A. Yeh, K. Fukami, “Broadcasting perturbations over turbulence,” in Causality in turbulence and transition, Madrid, Spain, May 2022. [PDF] [Video]
K. Fukami, V. Godavarthi, C.-A. Yeh, K. Taira, “Network broadcast analysis of airfoil wakes,” in 15th Southern California Flow Physics Symposium (SoCal Fluids XV), Los Angeles, California, USA, Apr 2022.
Y. Zhong, K. Fukami, B. An, K. Taira, “Machine-learning-based flow reconstruction of gust vortex-airfoil wake interactions,” in 15th Southern California Flow Physics Symposium (SoCal Fluids XV), Los Angeles, California, USA, Apr 2022.
V. Anantharaman, J. Feldkamp, K. Fukami, K. Taira, “Image and video compression of fluid flow data,” in 15th Southern California Flow Physics Symposium (SoCal Fluids XV), Los Angeles, California, USA, Apr 2022.
[Invited] K. Fukami, Y. Zhong, K. Taira, “Flow field reconstruction from sparse sensors with neural networks: Progress and outlook,” in the Advanced Modeling & Simulations seminar at the University of Texas at El Paso (UTEP) - Multi-Scale/Physics Computational Laboratory, USA, Apr 2022. [PDF]
[Invited] K. Fukami, R. Maulik, N. Ramachandra, M. Morimoto, R. Vinuesa, K. Fukagata, K. Taira, “Reconstructing turbulence with deep learning: uncertainty quantification and outlook,” in SIAM Conference on Uncertainty Quantification (UQ22), Atlanta, Georgia, USA, Apr 2022.
[Invited] K. Fukami, Y. Zhong, K. Taira, “Flow field reconstruction from sparse sensors with machine learning,” in the seminar at Sorbonne University, Paris, France, Apr 2022.
[Invited] K. Fukami, K. Fukagata, K. Taira, “Reconstructing turbulent flows with machine-learning-based super-resolution analysis,” in National Science Foundation AI Planning Institute for Data Driven Physics’ Workshop on “AI Super-Resolution Simulations: from Climate Science to Cosmology”, Pittsburgh, Pennsylvania, USA, Feb 2022.
T. Nakamura, K. Fukami, K. Fukagata, “Convolutional neural network-based global field recovery from sparse sensors of transitional boundary layer flow,” in 35th CFD symposium, online presentation, Dec 2021.
M. Morimoto, K. Fukami, R. Maulik, R. Vinuesa, K. Fukagata, “Model form uncertainty quantification of neural network-based fluid flow estimation,” in 35th CFD symposium, online presentation, Dec 2021.
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
[Invited] K. Fukami, R. Maulik, N. Ramachandra, K. Fukagata, K. Taira, “Global field reconstruction from sparse sensors with Voronoi tessellation-assisted convolutional neural network,” in Remote Colloquium on Vortex Dominated Flows (ReCoVor), online presentation, Dec 2021.
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.
M. Morimoto, K. Fukami, K. Zhang, K. Fukagata, “Towards practical uses of supervised neural networks for fluid flow regressions,” in Mechanistic Machine Learning and Digital Twins for Computational Science, Engineering & Technology (MMLDT-CSET2021), San Diego, USA, Sep 2021.
S. Kanehira, K. Fukami, K. Hasegawa, T. Nakamura, M. Morimoto, K. Fukagata, “Latent space based feedback control design: Machine-learning-based reduced-order modeling of unsteady fluid flows,” in Mechanistic Machine Learning and Digital Twins for Computational Science, Engineering & Technology (MMLDT-CSET2021), San Diego, USA, Sep 2021.
M. Matsuo, T. Nakamura, M. Morimoto, K. Fukami, K. Fukagata, “Convolutional neural network based three-dimensional fluid flow recovery from two-dimensional sectional data with super resolution based data augmentation,” in Mechanistic Machine Learning and Digital Twins for Computational Science, Engineering & Technology (MMLDT-CSET2021), San Diego, USA, Sep 2021.
T. Nakamura, K. Fukami, K. Fukagata, “Data-driven reduced-order modeling for turbulent flow forecast: neural networks and sparse regressions,” in Mechanistic Machine Learning and Digital Twins for Computational Science, Engineering & Technology (MMLDT-CSET2021), San Diego, USA, Sep 2021.
N. Moriya, M. Morimoto, K. Fukami, K. Hasegawa, K. Fukagata, “Hierarchical neural network for spatial reconstruction of fluid flow fields,” in The Japan Society of Fluid Mechanics Annual Meeting 2021, online presentation, Sep 2021.
K. Fukami, T. Murata, K. Zhang, S. Kanehira, K. Fukagata, “Time-series analysis of fluid flow dynamics using latent variables with sparse regressions: towards data-driven flow controls,” in JSME Mechanical Engineering Congress, 2021 Japan (MECJ-21), online, Sep 2021.
T. Nakamura, K. Fukami, K. Fukagata, “Fluid flow state estimation from sparse sensor measurements using convolutional neural network,” in JSME Mechanical Engineering Congress, 2021 Japan (MECJ-21), online, Sep 2021.
K. Hasegawa, K. Fukami, K. Fukagata, “Applications of convolutional neural network-based nonlinear mode decomposition to three-dimensional fluid flows,” in JSME Mechanical Engineering Congress, 2021 Japan (MECJ-21), online, Sep 2021.
K. Fukami, T. Murata, K. Fukagata, “Extracting nonlinear dynamics of low-dimensionalized flows”, in 25th International Congress of Theoretical and Applied Mechanics (XXV ICTAM), online, Aug 2021.
K. Hasegawa, K. Fukami, K. Fukagata, “Demonstration of machine learning-based reduced order modeling using unsteady flows around bluff bodies with various shapes”, in 25th International Congress of Theoretical and Applied Mechanics (XXV ICTAM), online, Aug 2021.
K. Fukami, K. Hasegawa, T. Nakamura, S. Kanehira, K. Fukagata, “Latent variable-based analysis with machine learning for reduced-order modeling and control of fluid flows,” in 16th U.S. National Congress on Computational Mechanics, online, Jul 2021.
M. Morimoto, K. Fukami, K. Zhang, A. G. Nair, K. Fukagata, “Parameter influence of supervised/unsupervised use of convolutional neural networks for fluid flow analyses,” in 16th U.S. National Congress on Computational Mechanics, online, Jul 2021.
T. Nakamura, K. Fukami, K. Fukagata, “Error-curve analysis of neural network and linear stochastic estimation for fluid flow problems,” in 16th U.S. National Congress on Computational Mechanics, online, Jul 2021.
K. Fukami, K. Taira, “Machine-learned invariant map for turbulent flow analysis and modeling: interpolation and extrapolation,” in Machine learning methods for prediction and control of separated turbulent flows, online, Jun 2021.
M. Matsuo, T. Nakamura, M. Morimoto, K. Fukami, K. Fukagata, “2D-3D CNN: Enabling neural networks for effective fluid data handling,” in 22nd Workshop on Turbulence Control, online presentation, Jun 2021.
M. Matsuo, K. Fukami, T. Nakamura, M. Morimoto, K. Fukagata, “Supervised convolutional networks for volumetric data enrichment from limited sectional data with adaptive super resolution,” in International Conference on Learning Representation (ICLR) workshop, Deep Learning for Simulation (SIMDL), May 2021.
T. Nakamura, K. Fukami, K. Fukagata, “Clues for noise robustness of state estimation: Error-curve quest of neural network and linear regression,” in International Conference on Learning Representation (ICLR) workshop, Deep Learning for Simulation (SIMDL), May 2021.
K. Fukami, R. Maulik, N. Ramachandra, K. Fukagata, K. Taira, “Voronoi tessellation-assisted convolutional neural network for flow field reconstruction from sparse sensors,” in 14th Southern California Flow Physics Symposium (SoCal Fluids XIV), online presentation, Apr 2021.
[Invited (Keynote)] K. Fukami, R. Maulik, N. Ramachandra, K. Fukagata, K. Taira, “Voronoi tessellation-aided machine learning for fluid flow data recovery from moving sparse sensors,” in 21st Workshop on Turbulence Control, online presentation, Mar 2021.
T. Nakamura, K. Fukami, K. Fukagata, “Bends of weight surfaces for noise robustness: linear and nonlinear fluid flow regressions,” in 21st Workshop on Turbulence Control, online presentation, Mar 2021.
[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. [Google Drive]
M. Morimoto, K. Fukami, T. Nakamura, K. Fukagata, “Neural network-based anomaly detections for nonlinear dynamical systems,” in 27th JSME Kanto Union conference, online, Mar 2021.
T. Nakamura, K. Fukami, K. Fukagata, “Machine learning based state estimation of turbulent flows: robustness for noisy input,” in 27th JSME Kanto Union conference, online, Mar 2021.
N. Moriya, K. Fukami, Y. Nabae, M. Morimoto, T. Nakamura, K. Fukagata, “Supervised machine learning based data-driven wall modeling for large-eddy simulation in a turbulent channel flow,” in 60th JSME Kanto Student Union Conference, Tokyo, Japan, Mar 2021.
M. Matsuo, M. Morimoto, T. Nakamura, K. Fukami, K. Fukagata, “Convolutional neural network based three-dimensional data reconstruction from two-dimensional sectional data with adaptive sampling,” in 60th JSME Kanto Student Union Conference, Tokyo, Japan, Mar 2021.
S. Kanehira, K. Fukami, K. Hasegawa, T. Nakamura, M. Morimoto, K. Fukagata, “Feedback control of unsteady fluid flows using an autoencoder with sparse identification of nonlinear dynamics,” in 60th JSME Kanto Student Union Conference, Tokyo, Japan, Mar 2021.
[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. [Google Drive]
[Invited] M. Morimoto, K. Fukami, K. Hasegawa, T. Nakamura, K. Fukagata, “Autoencoder based extraction of low-dimensional manifolds in fluid flows”, in SIAM Conference on Computational Science and Engineering (CSE21), Fort Worth, Texas, USA, Mar 2021.
[Invited] K. Fukami, K. Taira, M. Morimoto, K. Fukagata, “Convolutional neural network based fluid data enrichment for numerical and experimental studies”, in SIAM Conference on Computational Science and Engineering (CSE21), Fort Worth, Texas, USA, Mar 2021.
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. [Slideslive]
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. [Slideslive]
T. Nakamura, K. Fukami, K. Fukagata, “Machine learning-aided state estimation in a turbulent channel flow and its robustness for sensor information,” in 34th CFD symposium, Okinawa, Japan, Dec 2020.
N. Moriya, K. Fukami, Y. Nabae, M. Morimoto, T. Nakamura, K. Fukagata, “Supervised machine learning for wall-modeling in large eddy simulation of turbulent channel flows,” in 34th CFD symposium, Okinawa, Japan, Dec 2020.
M. Matsuo, M. Morimoto, T. Nakamura, K. Fukami, K. Fukagata, “Three-dimensional flow field reconstruction from two-dimensional sectional data using machine learning,” in 34th CFD symposium, Okinawa, Japan, Dec 2020.
M. Morimoto, K. Fukami, K. Zhang, K. Fukagata, “Toward practical machine learning and fluid flow regressions: perspective on interpretability and generalizability,” in 34th CFD symposium, Okinawa, Japan, Dec 2020.
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. [Youtube]
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. [Youtube]
S. Kanehira, K. Fukami, K. Hasegawa, T. Nakamura, M. Morimoto, K. Fukagata, “Toward latent space based feedback control with CNN-SINDy reduced order modeling of unsteady fluid flows,” in 20th Workshop on Turbulence Control, online presentation, Dec 2020.
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. [Youtube]
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. [Box]
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. [Box]
T. Nakamura, K. Fukami, K. Fukagata, “Extraction of nonlinear modes in fluid flows with a hierarchical convolutional neural network autoencoder,” in The Japan Society of Fluid Mechanics Annual Meeting 2020, online presentation, Sep 2020.
M. Morimoto, K. Fukami, K. Hasegawa, T. Nakamura, K. Fukagata, “Investigation of autoencoder-based low dimensionalization for various flow fields,” in The Japan Society of Fluid Mechanics Annual Meeting 2020, online presentation, Sep 2020.
N. Moriya, K. Fukami, Y. Nabae, M. Morimoto, T. Nakamura, K. Fukagata, “Large eddy simulation with a data-oriented wall model in turbulent channel flow,” in 19th Workshop on Turbulence Control, online presentation, Sep 2020.
M. Morimoto, K. Fukami, K. Fukagata, “ML-PIV: Convolutional neural network based velocity estimator for imperfect particle images,” in 18th Workshop on Turbulence Control, online presentation, Jun 2020.
M. Morimoto, K. Fukami, K. Hasegawa, T. Murata, H. Murakami, K. Fukagata, “Proposal of machine learning based particle image velocimetry,” in 59th JSME Kanto Student Union Conference, Tokyo, Japan, Mar 2020.
T. Nakamura, K. Fukami, K. Hasegawa, T. Murata, Y. Nabae, K. Fukagata, “Application of machine learning for reduced order modeling of turbulent channel flow,” in 59th JSME Kanto Student Union Conference, Tokyo, Japan, Mar 2020.
K. Fukami, Y. Nabae, K. Kawai, K. Fukagata, “A machine-learned turbulence generator for the channel flow,” in 2nd Pacific Rim Thermal Engineering Conference, Hawaii, USA, Dec 2019.
M. Morimoto, K. Fukami, K. Hasegawa, T. Murata, H. Murakami, K. Fukagata, “Improvement of PIV by data augmentation based on machine learning,” in 33rd CFD symposium, Hokkaido, Japan, Nov 2019.
T. Nakamura, K. Fukami, K. Hasegawa, T. Murata, Y. Nabae, K. Fukagata, “Machine learning of turbulent channel flows using autoencoders,” in 33rd CFD symposium, Hokkaido, Japan, Nov 2019.
K. Fukami, K. Fukagata, K. Taira, “Space-time recovery of high-resolution turbulent flow fields with machine learning based super resolution”, in 72nd Annual Meeting of the APS Division of Fluid Dynamics, Seattle, USA, Nov 2019.
K. Fukami, K. Fukagata, K. Taira, “Machine-learning-based super-resolution analysis for spatio-temporal data reconstruction of fluid flows”, in 17th Workshop on Turbulence Control, Tokyo, Japan, Nov 2019.
K. Fukami, K. Fukagata, K. Taira, “Machine-learned super-resolution analysis of three dimensional turbulent channel flow”, in The Japan Society of Mechanical Engineers Fluids Engineering Conference 2019, Aichi, Japan, Nov 2019.
K. Fukami, K. Fukagata, K. Taira, “Applications of machine-learned super-resolution algorithm for two-dimensional flow fields”, in The Japan Society of Fluid Mechanics Annual Meeting 2019, Tokyo, Japan, Sep 2019.
K. Fukami, K. Fukagata, K. Taira, “Super-resolution analysis with machine learning for low-resolution flow data”, in 11th International Symposium on Turbulence and Shear Flow Phenomena (TSFP11), Southampton, UK, July–Aug 2019.
K. Hasegawa, K. Fukami, T. Murata, K. Fukagata, “Data-driven reduced order modeling of flows around two-dimensional bluff bodies flow of various shapes”, in ASME-JSME-KSME Joint Fluids Engineering Conference 2019, San Francisco, USA, July–Aug 2019.
T. Murata, K. Fukami, K. Fukagata, “CNN/SINDy based reduced order modeling of unsteady flow fields”, in ASME-JSME-KSME Joint Fluids Engineering Conference 2019, San Francisco, USA, July–Aug 2019.
K. Fukami, K. Fukagata, K. Taira, “Image-based super-resolution analysis with machine learning for two-dimensional turbulence”, in 13th Southern California Flow Physics Symposium (SoCal Fluids XIII), Santa Barbara, USA, April 2019.
K. Hasegawa, K. Fukami, T. Murata, K. Fukagata, “Prediction of unsteady flows using machine-learned reduced order model,” in 58th JSME Kanto Student Union Conference, Chiba, Japan, Mar 2019.
K. Hasegawa, T. Murata, K. Fukami, K. Fukagata, “Machine learning-based prediction of flows around a circular cylinder at different Reynolds numbers,” in 15th Workshop on Turbulence Control, Tokyo, Japan, Jan 2019.
T. Murata, K. Fukami, K. Fukagata, “Extraction of low dimensional modes in a flow around a circular cylinder and prediction of their temporal evolutions using machine learning,” in 32nd CFD symposium, Tokyo, Japan, Dec 2018.
K. Hasegawa, K. Fukami, T. Murata, K. Fukagata, “Prediction of the Reynolds number dependency of flow around circular cylinder using machine learning,” in 32nd CFD symposium, Tokyo, Japan, Dec 2018.
T. Murata, K. Fukami, K. Fukagata, “Extraction of low dimensional modes in a flow around a circular cylinder and prediction of their temporal evolutions,” in 14th Workshop on Turbulence Control, Tokyo, Japan, Oct 2018.
K. Fukami, “Introduction about Fluid dynamics & Machine Learning,” in 13th Workshop on Turbulence Control, Tokyo, Japan, May 2018.
K. Fukami, K. Kawai, K. Fukagata, “Proposal of an inflow turbulence generator using machine learning,” in 57th JSME Kanto Student Union Conference, Tokyo, Japan, Mar 2018.
K. Fukami, K. Kawai, K. Fukagata,“Proposal of a turbulence generator using machine learning,” in 12th Workshop on Turbulence Control, Tokyo, Japan, Jan 2018.
*All recorded talks are available on here.