Geometry understanding is a core concept of computer-aided design and engineering (CAD/CAE). Deep neural networks have increasingly shown success as a method of processing complex inputs to achieve abstract tasks. This work revisits a generic and relatively simple approach to 3D deep learning - a point-based graph neural network - and develops best-practices and modifications to alleviate traditional drawbacks. It is shown that these methods should not be discounted for CAD tasks; with proper implementation, they can be competitive with more specifically designed approaches. Through an additive study, this work investigates how the boundary representation data can be fully utilised by leveraging the flexibility of point-based graph networks. The final configuration significantly improves on the predictive accuracy of a standard PointNet++ network across multiple CAD model segmentation datasets and achieves state-of-the-art performance on the MFCAD++ machining features dataset. The proposed modifications leave the core neural network unchanged and results also suggest that they can be applied to other point-based approaches.

In this paper, we propose a technique based on genetic programming (GP) for meshfree solution of elliptic partial differential equations. We employ the least-squares collocation principle to define an appropriate objective function, which is optimized using GP. Two approaches are presented for the repair of the symbolic expression for the field variables evolved by the GP algorithm to ensure that the governing equations as well as the boundary conditions are satisfied. In the case of problems defined on geometrically simple domains, we augment the solution evolved by GP with additional terms, such that the boundary conditions are satisfied by construction. To satisfy the boundary conditions for geometrically irregular domains, we combine the GP model with a radial basis function network. We improve the computational efficiency and accuracy of both techniques with gradient boosting, a technique originally developed by the machine learning community. Numerical studies are presented for operator problems on regular and irregular boundaries to illustrate the performance of the proposed algorithms.

In this paper, we present a novel surrogate-assisted evolutionary optimization framework for solving computationally expensive problems. The proposed framework uses computationally cheap hierarchical surrogate models constructed through online learning to replace the exact computationally expensive objective functions during evolutionary search. At the first level, the framework employs a data-parallel Gaussian process based global surrogate model to filter the evolutionary algorithm (EA) population of promising individuals. Subsequently, these potential individuals undergo a memetic search in the form of Lamarckian learning at the second level. The Lamarckian evolution involves a trust-region enabled gradient-based search strategy that employs radial basis function local surrogate models to accelerate convergence. Numerical results are presented on a series of benchmark test functions and on an aerodynamic shape design problem. The results obtained suggest that the proposed optimization framework converges to good designs on a limited computational budget. Furthermore, it is shown that the new algorithm gives significant savings in computational cost when compared to the traditional evolutionary algorithm and other surrogate assisted optimization frameworks

This paper presents the experiences gained from ongoing research collaboration between the School of Computer Engineering at Nanyang Technological University and the Southampton e-Science centre at the University of Southampton using a service-oriented approach for complex engineering design optimisation. The service-oriented approach enables programmatic collaboration to be realized while maintaining the autonomy of individual codes at the different institutes and organizations. In the current work, a genetic algorithm optimisation logic implemented as a grid service at Southampton is used to drive the design search process, while the aerodynamic analysis code located in Singapore is used to evaluate the objective function of the design points. Experience gathered from the current study on airfoil shape optimisation is valuable for establishing effective, efficient and customised programmatic links between institutions to solve complicated engineering design problems.

Over the last decade, memetic algorithms (MAs) have relied on the use of a variety of different methods as the local improvement procedure. Some recent studies on the choice of local search method employed have shown that this choice significantly affects the efficiency of problem searches. Given the restricted theoretical knowledge available in this area and the limited progress made on mitigating the effects of incorrect local search method choice, we present strategies for MA control that decide, at runtime, which local method is chosen to locally improve the next chromosome. The use of multiple local methods during a MA search in the spirit of Lamarckian learning is here termed Meta-Lamarckian learning. Two adaptive strategies for Meta-Lamarckian learning are proposed in this paper. Experimental studies with Meta-Lamarckian learning strategies on continuous parametric benchmark problems are also presented. Further, the best strategy proposed is applied to a real-world aerodynamic wing design problem and encouraging results are obtained. It is shown that the proposed approaches aid designers working on complex engineering problems by reducing the probability of employing inappropriate local search methods in a MA, while at the same time, yielding robust and improved design search performance.

We are developing a Grid Enabled Optimisation and Design Search system (GEODISE). It offers grid-based access to a state-of-the-art collection of optimisation and design search tools, industrial strength analysis codes, and distributed computing and data resources.

This paper proposes an enabling data parallel local learning methodology for handling large data regression through the Gaussian Process (GP) modeling paradigm. The proposed model achieves parallelism by employing a specialized compactly supported covariance function defined over spatially localized clusters. The associated load balancing constraints arising from data parallelism are satisfied using a novel greedy clustering algorithm, GeoClust producing balanced clusters localized in space. Further, the use of the proposed covariance function as a building block for GP models is shown to decompose the maximum likelihood estimation problem into smaller decoupled subproblems. The attendant benefits which include a significant reduction in training complexity, as well as sparse predictive models for the posterior mean and variance make the present scheme extremely attractive. Experimental investigations on real and synthetic data demonstrate that the current approach can consistently outperform the state-of-the-art Bayesian Committee Machine (BCM) which employs a random data partitioning strategy. Finally, extensive evaluations over a grid-based computational infrastructure using the NetSolve distributed computing system show that the present approach scales well with data and could potentially be used in large-scale data mining applications.

In many problems in science and engineering, it is often the case that there exist a number of computational models to simulate the problem at hand. These models are usually trade-offs between accuracy and computational expense. Given a limited computation budget, there is need to develop a framework for selecting between different models in a sensible fashion during the search. The method proposed here is based on the construction of a heteroassociative mapping to estimate the differences between models, and using this information to guide the search. The proposed framework is tested on the problem of minimizing the transmitted vibration energy in a satellite boom.