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Volume 3 Issue 7, July 2021

Volume 3 Issue 7

Learning physical displacement fields with deep optical flow

Analysing complex flow dynamics is important in a wide range of problems in areas such as automotive, aerospace and biomedical engineering. Particle image velocimetry (PIV) is a key technique for visualizing and computing the velocity components of flow fields. Conventionally, manually designed algorithms are needed to process PIV measurements, but deep learning-based optical flow estimators are developed by Lagemann et al. that promise to be general, largely automated and to provide a high spatial resolution, which allows one to study very fine velocity fluctuations. The cover image highlights such a dense displacement field for a turbulent boundary layer predicted by the proposed optical flow learning model.

See Lagemann et al.

Image: Christian Lagemann, Institute of Aerodynamics Aachen. Cover design: Lauren Heslop.


  • Editorial |

    The COVID-19 pandemic is not over and the future is uncertain, but there has lately been a semblance of what life was like before. As thoughts turn to the possibility of a summer holiday, we offer suggestions for books and podcasts on AI to refresh the mind.


  • Perspective |

    Traditional sensing techniques apply computational analysis at the output of the sensor hardware to separate signal from noise. A new, more holistic and potentially more powerful approach proposed in this Perspective is designing intelligent sensor systems that ‘lock-in’ to optimal sensing of data, making use of machine leaning strategies.

    • Zachary Ballard
    • Calvin Brown
    • Aydogan Ozcan
  • Perspective |

    As highly automated systems become pervasive in society, enforceable governance principles are needed to ensure safe deployment. This Perspective proposes a pragmatic approach where independent audit of AI systems is central. The framework would embody three AAA governance principles: prospective risk Assessments, operation Audit trails and system Adherence to jurisdictional requirements.

    • Gregory Falco
    • Ben Shneiderman
    • Zee Kin Yeong


  • Article |

    Experimental benchmarks such as ImageNet and Atari games play an important part in advancing artificial intelligence research. An analysis of results and papers linked to 25 popular benchmarks shows that research dynamics beyond conventional co-authorship has developed in this area.

    • Fernando Martínez-Plumed
    • Pablo Barredo
    • José Hernández-Orallo
  • Article |

    Calcium imaging is a valuable tool for recording in vivo neural activity, but the task of extracting signals of individual neurons is computationally challenging. Bao and colleagues present a U-Net-based method that is both accurate and fast enough to potentially allow real-time processing and closed-loop experiments.

    • Yijun Bao
    • Somayyeh Soltanian-Zadeh
    • Yiyang Gong
  • Article |

    In the last few years, computational protein structure prediction has greatly advanced by combining deep learning including convolutional residual networks (ResNet) with co-evolution data. A new study finds that using deeper and wider ResNets improves predictions in the absence of co-evolution information, suggesting that the ResNets do not not simply de-noise co-evolution signals, but instead may learn important protein sequence–structure relationships.

    • Jinbo Xu
    • Matthew McPartlon
    • Jin Li
  • Article |

    The urgency of the developing COVID-19 epidemic has led to a large number of novel diagnostic approaches, many of which use machine learning. DeGrave and colleagues use explainable AI techniques to analyse a selection of these approaches and find that the methods frequently learn to identify features unrelated to the actual disease.

    • Alex J. DeGrave
    • Joseph D. Janizek
    • Su-In Lee


  • Article |

    Neural networks are becoming increasingly popular for applications in various domains, but in practice, further methods are necessary to make sure the models are learning patterns that agree with prior knowledge about the domain. A new approach introduces an explanation method, called ‘expected gradients’, that enables training with theoretically motivated feature attribution priors, to improve model performance on real-world tasks.

    • Gabriel Erion
    • Joseph D. Janizek
    • Su-In Lee
  • Article |

    Monoclonalization, the isolation and expansion of a single cell derived from a cultured population, is an essential step in large-scale human cell culture and experiments. A new deep learning-based workflow called Monoqlo automatically detects colony presence and identifies clonality from cellular imaging, enabling single-cell selection protocols to be scalable while minimizing technical variability.

    • Brodie Fischbacher
    • Sarita Hedaya
    • Daniel Paull
  • Article |

    Particle image velocimetry is an imaging technique to determine the velocity components of flow fields, of use in a range of complex engineering problems including in environmental, aerospace and biomedical engineering. A recurrent neural network-based approach for learning displacement fields in an end-to-end manner is applied to this technique and achieves state-of-the-art accuracy and, moreover, allows generalization to new data, eliminating the need for traditional handcrafted models.

    • Christian Lagemann
    • Kai Lagemann
    • Wolfgang Schröder

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