308 research outputs found
Towards Efficient and Effective Deep Clustering with Dynamic Grouping and Prototype Aggregation
Previous contrastive deep clustering methods mostly focus on instance-level
information while overlooking the member relationship within groups/clusters,
which may significantly undermine their representation learning and clustering
capability. Recently, some group-contrastive methods have been developed,
which, however, typically rely on the samples of the entire dataset to obtain
pseudo labels and lack the ability to efficiently update the group assignments
in a batch-wise manner. To tackle these critical issues, we present a novel
end-to-end deep clustering framework with dynamic grouping and prototype
aggregation, termed as DigPro. Specifically, the proposed dynamic grouping
extends contrastive learning from instance-level to group-level, which is
effective and efficient for timely updating groups. Meanwhile, we perform
contrastive learning on prototypes in a spherical feature space, termed as
prototype aggregation, which aims to maximize the inter-cluster distance.
Notably, with an expectation-maximization framework, DigPro simultaneously
takes advantage of compact intra-cluster connections, well-separated clusters,
and efficient group updating during the self-supervised training. Extensive
experiments on six image benchmarks demonstrate the superior performance of our
approach over the state-of-the-art. Code is available at
https://github.com/Regan-Zhang/DigPro
Modelling the Self-similarity in Complex Networks Based on Coulomb's Law
Recently, self-similarity of complex networks have attracted much attention.
Fractal dimension of complex network is an open issue. Hub repulsion plays an
important role in fractal topologies. This paper models the repulsion among the
nodes in the complex networks in calculation of the fractal dimension of the
networks. The Coulomb's law is adopted to represent the repulse between two
nodes of the network quantitatively. A new method to calculate the fractal
dimension of complex networks is proposed. The Sierpinski triangle network and
some real complex networks are investigated. The results are illustrated to
show that the new model of self-similarity of complex networks is reasonable
and efficient.Comment: 25 pages, 11 figure
Multi-fractal analysis of weighted networks
In many real complex networks, the fractal and self-similarity properties
have been found. The fractal dimension is a useful method to describe fractal
property of complex networks. Fractal analysis is inadequate if only taking one
fractal dimension to study complex networks. In this case, multifractal
analysis of complex networks are concerned. However, multifractal dimension of
weighted networks are less involved. In this paper, multifractal dimension of
weighted networks is proposed based on box-covering algorithm for fractal
dimension of weighted networks (BCANw). The proposed method is applied to
calculate the fractal dimensions of some real networks. Our numerical results
indicate that the proposed method is efficient for analysis fractal property of
weighted networks
Physically Interpretable Feature Learning and Inverse Design of Supercritical Airfoils
Machine-learning models have demonstrated a great ability to learn complex
patterns and make predictions. In high-dimensional nonlinear problems of fluid
dynamics, data representation often greatly affects the performance and
interpretability of machine learning algorithms. With the increasing
application of machine learning in fluid dynamics studies, the need for
physically explainable models continues to grow. This paper proposes a feature
learning algorithm based on variational autoencoders, which is able to assign
physical features to some latent variables of the variational autoencoder. In
addition, it is theoretically proved that the remaining latent variables are
independent of the physical features. The proposed algorithm is trained to
include shock wave features in its latent variables for the reconstruction of
supercritical pressure distributions. The reconstruction accuracy and physical
interpretability are also compared with those of other variational
autoencoders. Then, the proposed algorithm is used for the inverse design of
supercritical airfoils, which enables the generation of airfoil geometries
based on physical features rather than the complete pressure distributions. It
also demonstrates the ability to manipulate certain pressure distribution
features of the airfoil without changing the others
Gene regulatory network and epigenetic reprogramming of pig primordial germ cells
Primordial germ cells (PGC) are the precursors of the gametes. The mechanisms of PGC induction, specification and development are very well characterized in rodents, however recent investigations have demonstrated that the mechanisms of germ cell development differ significantly between mice and humans. Since the knowledge of PGC development in non-rodents is very limited, and early human embryos cannot be accessed it is important to establish a new model for PGC development with relevance to humans. In this thesis, I use pig embryo as a model for investigating PGC development in non-rodent mammals. The expression profile of key transcription factors, epigenetic reprograming and the role of signalling pathways were investigated during specification and development of pig PGCs. The key findings are: A- Specification of porcine PGC occurs after the onset of gastrulation, requiring BMP4 signalling. B- WNT signalling is required for the generation of precursors competent for germline commitment; however it is downregulated after PGCs are specified. WNT downregulation could be modulated by SOX17, the earliest gene expressed in pig PGCs. C- Epigenetic reprogramming of DNA and histone marks starts in pre-migratory porcine PGC. Furthermore, chromatin dynamics in pig gonadal PGCs resemble that of humans but differs to that of mice. D- The expression profile of transcription factors of porcine PGC is similar to that of humans, but different to mouse PGC.
In conclusion, this study has highlighted critical differences between mice and humans/pigs during germ cell specification. I provide evidence that the pig embryo is a useful model for the study of human development, and future studies will need to be directed to re-evaluate concepts of cell differentiation and early lineage commitment established in mice that may not apply to humans
Study of transfer learning from 2D supercritical airfoils to 3D transonic swept wings
Machine learning has been widely utilized in fluid mechanics studies and
aerodynamic optimizations. However, most applications, especially flow field
modeling and inverse design, involve two-dimensional flows and geometries. The
dimensionality of three-dimensional problems is so high that it is too
difficult and expensive to prepare sufficient samples. Therefore, transfer
learning has become a promising approach to reuse well-trained two-dimensional
models and greatly reduce the need for samples for three-dimensional problems.
This paper proposes to reuse the baseline models trained on supercritical
airfoils to predict finite-span swept supercritical wings, where the simple
swept theory is embedded to improve the prediction accuracy. Two baseline
models for transfer learning are investigated: one is commonly referred to as
the forward problem of predicting the pressure coefficient distribution based
on the geometry, and the other is the inverse problem that predicts the
geometry based on the pressure coefficient distribution. Two transfer learning
strategies are compared for both baseline models. The transferred models are
then tested on the prediction of complete wings. The results show that transfer
learning requires only approximately 500 wing samples to achieve good
prediction accuracy on different wing planforms and different free stream
conditions. Compared to the two baseline models, the transferred models reduce
the prediction error by 60% and 80%, respectively
Fast buffet onset prediction and optimization method based on a pre-trained flowfield prediction model
The transonic buffet is a detrimental phenomenon occurs on supercritical
airfoils and limits aircraft's operating envelope. Traditional methods for
predicting buffet onset rely on multiple computational fluid dynamics
simulations to assess a series of airfoil flowfields and then apply criteria to
them, which is slow and hinders optimization efforts. This article introduces
an innovative approach for rapid buffet onset prediction. A machine-learning
flowfield prediction model is pre-trained on a large database and then deployed
offline to replace simulations in the buffet prediction process for new airfoil
designs. Unlike using a model to directly predict buffet onset, the proposed
technique offers better visualization capabilities by providing users with
intuitive flowfield outputs. It also demonstrates superior generalization
ability, evidenced by a 32.5% reduction in average buffet onset prediction
error on the testing dataset. The method is utilized to optimize the buffet
performance of 11 distinct airfoils within and outside the training dataset.
The optimization results are verified with simulations and proved to yield
improved samples across all cases. It is affirmed the pre-trained flowfield
prediction model can be applied to accelerate aerodynamic shape optimization,
while further work still needs to raise its reliability for this
safety-critical task.Comment: 44 pages, 20 figure
Flowfield prediction of airfoil off-design conditions based on a modified variational autoencoder
Airfoil aerodynamic optimization based on single-point design may lead to
poor off-design behaviors. Multipoint optimization that considers the
off-design flow conditions is usually applied to improve the robustness and
expand the flight envelope. Many deep learning models have been utilized for
the rapid prediction or reconstruction of flowfields. However, the flowfield
reconstruction accuracy may be insufficient for cruise efficiency optimization,
and the model generalization ability is also questionable when facing airfoils
different from the airfoils with which the model has been trained. Because a
computational fluid dynamic evaluation of the cruise condition is usually
necessary and affordable in industrial design, a novel deep learning framework
is proposed to utilize the cruise flowfield as a prior reference for the
off-design condition prediction. A prior variational autoencoder is developed
to extract features from the cruise flowfield and to generate new flowfields
under other free stream conditions. Physical-based loss functions based on
aerodynamic force and conservation of mass are derived to minimize the
prediction error of the flowfield reconstruction. The results demonstrate that
the proposed model can reduce the prediction error on test airfoils by 30%
compared to traditional models. The physical-based loss function can further
reduce the prediction error by 4%. The proposed model illustrates a better
balance of the time cost and the fidelity requirements of evaluation for cruise
and off-design conditions, which makes the model more feasible for industrial
applications
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