254,749 research outputs found
Towards simultaneous meta-modeling for both the output and input spaces in the context of design shape optimization using asynchronous high-performance computing
ECCOMAS PhD Olympiads 2013International audience. In this paper, we propose a simultaneous meta-modeling protocol for both input and output spaces. We perform a reparametrization of the input space using constrained shape interpolation by introducing the concept of an α-manifold of admissible meshed shapes. The output space is reduced using constrained Proper Orthogonal Decomposition. By simultaneously using meta-modeling for both spaces, we facilitate interactive design space exploration for the purpose of design. The proposed approach is applied to several industrial problems
Machine Learning for Metaverse-enabled Wireless Systems: Vision, Requirements, and Challenges
Today's wireless systems are posing key challenges in terms of quality of
service and quality of physical experience. Metaverse has the potential to
reshape, transform, and add innovations to the existing wireless systems. A
metaverse is a collective virtual open space that can enable wireless systems
using digital twins, digital avatars, and interactive experience technologies.
Machine learning (ML) is indispensable for modeling twins, avatars, and
deploying interactive experience technologies. In this paper, we present the
role of ML in enabling metaverse-based wireless systems. We identify and
discuss a set of key requirements for advancing ML in the metaverse-based
wireless systems. Moreover, we present a case study of distributed split
federated learning for efficiently training meta-space models. Finally, we
discuss the future challenges along with potential solutions
A Computational Intelligence Approach to System-of-Systems Architecting Incorporating Multi-Objective Optimization
A computational intelligence approach to system-of-systems architecting is developed using multi-objective optimization. Such an approach yields a set of optimal solutions (the Pareto set) which has both advantages and disadvantages. The primary benefit is that a set of solutions provides a picture of the optimal solution space that a single solution cannot. The primary difficulty is making use of a potentially infinite set of solutions. Therefore, a significant part of this approach is the development of a method to model the solution set with a finite number of points allowing the architect to intelligently choose a subset of optimal solutions based on criteria outside of the given objectives. The approach developed incorporates a meta-architecture, multi-objective genetic algorithm, and a corner search to identify points useful for modeling the solution space. This approach is then applied to a network centric warfare problem seeking the optimum selection of twenty systems. Finally, using the same problem, it is compared to a hybrid approach using single-objective optimization with a fuzzy logic assessor to demonstrate the advantage of multi-objective optimization
Polynomial-Chaos-based Kriging
Computer simulation has become the standard tool in many engineering fields
for designing and optimizing systems, as well as for assessing their
reliability. To cope with demanding analysis such as optimization and
reliability, surrogate models (a.k.a meta-models) have been increasingly
investigated in the last decade. Polynomial Chaos Expansions (PCE) and Kriging
are two popular non-intrusive meta-modelling techniques. PCE surrogates the
computational model with a series of orthonormal polynomials in the input
variables where polynomials are chosen in coherency with the probability
distributions of those input variables. On the other hand, Kriging assumes that
the computer model behaves as a realization of a Gaussian random process whose
parameters are estimated from the available computer runs, i.e. input vectors
and response values. These two techniques have been developed more or less in
parallel so far with little interaction between the researchers in the two
fields. In this paper, PC-Kriging is derived as a new non-intrusive
meta-modeling approach combining PCE and Kriging. A sparse set of orthonormal
polynomials (PCE) approximates the global behavior of the computational model
whereas Kriging manages the local variability of the model output. An adaptive
algorithm similar to the least angle regression algorithm determines the
optimal sparse set of polynomials. PC-Kriging is validated on various benchmark
analytical functions which are easy to sample for reference results. From the
numerical investigations it is concluded that PC-Kriging performs better than
or at least as good as the two distinct meta-modeling techniques. A larger gain
in accuracy is obtained when the experimental design has a limited size, which
is an asset when dealing with demanding computational models
UML to XML-Schema Transformation: a Case Study in Managing Alternative Model Transformations in MDA
In a Model Driven Architecture (MDA) software development process, models are\ud
repeatedly transformed to other models in order to finally achieve a set of models with enough details to implement a system. Generally, there are multiple ways to transform one model into another model. Alternative target models differ in their quality properties and the selection of a particular model is determined on the basis of specific requirements. Software engineers must be able to identify, compare and select the appropriate transformations within the given set of requirements. The current transformation languages used for describing and executing model transformations only provide means to specify the transformations but do not help to identify and select from the alternative transformations. In this paper we propose a process and a set of techniques for constructing a transformation space for a given transformation problem. The process uses a source model, its meta-model and the meta-model of the target as input and generates a transformation space. Every element in that space represents a transformation that produces a result that is an instance of the target meta-model. The requirements that must be fulfilled by the result are captured and represented in a quality model. We explain our approach using an illustrative example for transforming a platform independent model expressed in UML into platform specific models that represent XML schemas. A particular quality model of extensibility is presented in the paper
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