1,787 research outputs found
Particle swarm algorithm with adaptive constraint handling and integrated surrogate model for the management of petroleum fields
This paper deals with the development of effective techniques to automatically obtain the optimum management of petroleum fields aiming to increase the oil production during a given concession period of exploration. The optimization formulations of such a problem turn out to be highly multimodal, and may involve constraints. In this paper, we develop a robust particle swarm algorithm coupled with a novel adaptive constraint-handling technique to search for the global optimum of these formulations. However, this is a population-based method, which therefore requires a high number of evaluations of an objective function. Since the performance evaluation of a given management scheme requires a computationally expensive high-fidelity simulation, it is not practicable to use it directly to guide the search. In order to overcome this drawback, a Kriging surrogate model is used, which is trained offline via evaluations of a High-Fidelity simulator on a number of sample points. The optimizer then seeks the optimum of the surrogate model
From Continuous Dynamics to Graph Neural Networks: Neural Diffusion and Beyond
Graph neural networks (GNNs) have demonstrated significant promise in
modelling relational data and have been widely applied in various fields of
interest. The key mechanism behind GNNs is the so-called message passing where
information is being iteratively aggregated to central nodes from their
neighbourhood. Such a scheme has been found to be intrinsically linked to a
physical process known as heat diffusion, where the propagation of GNNs
naturally corresponds to the evolution of heat density. Analogizing the process
of message passing to the heat dynamics allows to fundamentally understand the
power and pitfalls of GNNs and consequently informs better model design.
Recently, there emerges a plethora of works that proposes GNNs inspired from
the continuous dynamics formulation, in an attempt to mitigate the known
limitations of GNNs, such as oversmoothing and oversquashing. In this survey,
we provide the first systematic and comprehensive review of studies that
leverage the continuous perspective of GNNs. To this end, we introduce
foundational ingredients for adapting continuous dynamics to GNNs, along with a
general framework for the design of graph neural dynamics. We then review and
categorize existing works based on their driven mechanisms and underlying
dynamics. We also summarize how the limitations of classic GNNs can be
addressed under the continuous framework. We conclude by identifying multiple
open research directions
Distributed Training of Graph Convolutional Networks
The aim of this work is to develop a fully-distributed algorithmic framework
for training graph convolutional networks (GCNs). The proposed method is able
to exploit the meaningful relational structure of the input data, which are
collected by a set of agents that communicate over a sparse network topology.
After formulating the centralized GCN training problem, we first show how to
make inference in a distributed scenario where the underlying data graph is
split among different agents. Then, we propose a distributed gradient descent
procedure to solve the GCN training problem. The resulting model distributes
computation along three lines: during inference, during back-propagation, and
during optimization. Convergence to stationary solutions of the GCN training
problem is also established under mild conditions. Finally, we propose an
optimization criterion to design the communication topology between agents in
order to match with the graph describing data relationships. A wide set of
numerical results validate our proposal. To the best of our knowledge, this is
the first work combining graph convolutional neural networks with distributed
optimization.Comment: Published on IEEE Transactions on Signal and Information Processing
over Network
Tools and Algorithms for the Construction and Analysis of Systems
This open access two-volume set constitutes the proceedings of the 27th International Conference on Tools and Algorithms for the Construction and Analysis of Systems, TACAS 2021, which was held during March 27 ā April 1, 2021, as part of the European Joint Conferences on Theory and Practice of Software, ETAPS 2021. The conference was planned to take place in Luxembourg and changed to an online format due to the COVID-19 pandemic. The total of 41 full papers presented in the proceedings was carefully reviewed and selected from 141 submissions. The volume also contains 7 tool papers; 6 Tool Demo papers, 9 SV-Comp Competition Papers. The papers are organized in topical sections as follows: Part I: Game Theory; SMT Verification; Probabilities; Timed Systems; Neural Networks; Analysis of Network Communication. Part II: Verification Techniques (not SMT); Case Studies; Proof Generation/Validation; Tool Papers; Tool Demo Papers; SV-Comp Tool Competition Papers
User preference extraction using dynamic query sliders in conjunction with UPS-EMO algorithm
One drawback of evolutionary multiobjective optimization algorithms (EMOA)
has traditionally been high computational cost to create an approximation of
the Pareto front: number of required objective function evaluations usually
grows high. On the other hand, for the decision maker (DM) it may be difficult
to select one of the many produced solutions as the final one, especially in
the case of more than two objectives.
To overcome the above mentioned drawbacks number of EMOA's incorporating the
decision makers preference information have been proposed. In this case, it is
possible to save objective function evaluations by generating only the part of
the front the DM is interested in, thus also narrowing down the pool of
possible selections for the final solution.
Unfortunately, most of the current EMO approaches utilizing preferences are
not very intuitive to use, i.e. they may require tweaking of unintuitive
parameters, and it is not always clear what kind of results one can get with
given set of parameters. In this study we propose a new approach to visually
inspect produced solutions, and to extract preference information from the DM
to further guide the search. Our approach is based on intuitive use of dynamic
query sliders, which serve as a means to extract preference information and are
part of the graphical user interface implemented for the efficient UPS-EMO
algorithm
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