3,595 research outputs found
Machine Learning for Fluid Mechanics
The field of fluid mechanics is rapidly advancing, driven by unprecedented
volumes of data from field measurements, experiments and large-scale
simulations at multiple spatiotemporal scales. Machine learning offers a wealth
of techniques to extract information from data that could be translated into
knowledge about the underlying fluid mechanics. Moreover, machine learning
algorithms can augment domain knowledge and automate tasks related to flow
control and optimization. This article presents an overview of past history,
current developments, and emerging opportunities of machine learning for fluid
mechanics. It outlines fundamental machine learning methodologies and discusses
their uses for understanding, modeling, optimizing, and controlling fluid
flows. The strengths and limitations of these methods are addressed from the
perspective of scientific inquiry that considers data as an inherent part of
modeling, experimentation, and simulation. Machine learning provides a powerful
information processing framework that can enrich, and possibly even transform,
current lines of fluid mechanics research and industrial applications.Comment: To appear in the Annual Reviews of Fluid Mechanics, 202
Domain Adaptation for Novel Imaging Modalities with Application to Prostate MRI
The need for training data can impede the adoption of novel imaging modalities for deep learning-based medical image analysis. Domain adaptation can mitigate this problem by exploiting training samples from an existing, densely-annotated source domain within a novel, sparsely-annotated target domain, by bridging the differences between the two domains. In this thesis we present methods for adapting between diffusion-weighed (DW)-MRI data from multiparametric (mp)-MRI acquisitions and VERDICT (Vascular, Extracellular and Restricted Diffusion for Cytometry in Tumors) MRI, a richer DW-MRI technique involving an optimized acquisition protocol for cancer characterization. We also show that the proposed methods are general and their applicability extends beyond medical imaging.
First, we propose a semi-supervised domain adaptation method for prostate lesion segmentation on VERDICT MRI. Our approach relies on stochastic generative modelling to translate across two heterogeneous domains at pixel-space and exploits the inherent uncertainty in the cross-domain mapping to generate multiple outputs conditioned on a single input. We further extend this approach to the unsupervised scenario where there is no labeled data for the target domain. We rely on stochastic generative modelling to translate across the two domains at pixel space and introduce two loss functions that promote semantic consistency.
Finally we demonstrate that the proposed approaches extend beyond medical image analysis and focus on unsupervised domain adaptation for semantic segmentation of urban scenes. We show that relying on stochastic generative modelling allows us to train more accurate target networks and achieve state-of-the-art performance on two challenging semantic segmentation benchmarks
Multi-level Feature Fusion-based CNN for Local Climate Zone Classification from Sentinel-2 Images: Benchmark Results on the So2Sat LCZ42 Dataset
As a unique classification scheme for urban forms and functions, the local
climate zone (LCZ) system provides essential general information for any
studies related to urban environments, especially on a large scale. Remote
sensing data-based classification approaches are the key to large-scale mapping
and monitoring of LCZs. The potential of deep learning-based approaches is not
yet fully explored, even though advanced convolutional neural networks (CNNs)
continue to push the frontiers for various computer vision tasks. One reason is
that published studies are based on different datasets, usually at a regional
scale, which makes it impossible to fairly and consistently compare the
potential of different CNNs for real-world scenarios. This study is based on
the big So2Sat LCZ42 benchmark dataset dedicated to LCZ classification. Using
this dataset, we studied a range of CNNs of varying sizes. In addition, we
proposed a CNN to classify LCZs from Sentinel-2 images, Sen2LCZ-Net. Using this
base network, we propose fusing multi-level features using the extended
Sen2LCZ-Net-MF. With this proposed simple network architecture and the highly
competitive benchmark dataset, we obtain results that are better than those
obtained by the state-of-the-art CNNs, while requiring less computation with
fewer layers and parameters. Large-scale LCZ classification examples of
completely unseen areas are presented, demonstrating the potential of our
proposed Sen2LCZ-Net-MF as well as the So2Sat LCZ42 dataset. We also
intensively investigated the influence of network depth and width and the
effectiveness of the design choices made for Sen2LCZ-Net-MF. Our work will
provide important baselines for future CNN-based algorithm developments for
both LCZ classification and other urban land cover land use classification
Bridging the Domain Gap for Multi-Agent Perception
Existing multi-agent perception algorithms usually select to share deep
neural features extracted from raw sensing data between agents, achieving a
trade-off between accuracy and communication bandwidth limit. However, these
methods assume all agents have identical neural networks, which might not be
practical in the real world. The transmitted features can have a large domain
gap when the models differ, leading to a dramatic performance drop in
multi-agent perception. In this paper, we propose the first lightweight
framework to bridge such domain gaps for multi-agent perception, which can be a
plug-in module for most existing systems while maintaining confidentiality. Our
framework consists of a learnable feature resizer to align features in multiple
dimensions and a sparse cross-domain transformer for domain adaption. Extensive
experiments on the public multi-agent perception dataset V2XSet have
demonstrated that our method can effectively bridge the gap for features from
different domains and outperform other baseline methods significantly by at
least 8% for point-cloud-based 3D object detection.Comment: Accepted by ICRA2023.Code: https://github.com/DerrickXuNu/MPD
Contrastive Image Synthesis and Self-supervised Feature Adaptation for Cross-Modality Biomedical Image Segmentation
This work presents a novel framework CISFA (Contrastive Image synthesis and
Self-supervised Feature Adaptation)that builds on image domain translation and
unsupervised feature adaptation for cross-modality biomedical image
segmentation. Different from existing works, we use a one-sided generative
model and add a weighted patch-wise contrastive loss between sampled patches of
the input image and the corresponding synthetic image, which serves as shape
constraints. Moreover, we notice that the generated images and input images
share similar structural information but are in different modalities. As such,
we enforce contrastive losses on the generated images and the input images to
train the encoder of a segmentation model to minimize the discrepancy between
paired images in the learned embedding space. Compared with existing works that
rely on adversarial learning for feature adaptation, such a method enables the
encoder to learn domain-independent features in a more explicit way. We
extensively evaluate our methods on segmentation tasks containing CT and MRI
images for abdominal cavities and whole hearts. Experimental results show that
the proposed framework not only outputs synthetic images with less distortion
of organ shapes, but also outperforms state-of-the-art domain adaptation
methods by a large margin
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