The development of a competence framework for engineering analysis and simulation

Engineering analysis and simulation has always played a significant role in the nuclear sector and its use continues to increase across all branches of industry. To remain competitive in an increasingly global environment and to ensure the safety and reliability of products, companies must prepare effectively for the challenges that new engineering simulation technologies will bring. Concerns surrounding the inappropriate use of simulation by staff without the appropriate competences persist, as analyses become more advanced, increasingly embracing more complex physical phenomena and interactions, often in an effort to model reality more faithfully. These trends and the associated competencies required, emphasize the need for life-long learning and continual staff development. Organisations clearly require a sufficient and ongoing supply of well-qualified engineers and the recently funded EASIT2 project is directly aimed at addressing and managing these issues

Transferring simulation skills from other industries to nuclear

Engineering analysis and simulation has always played a significant role in the nuclear sector and its use continues to increase across all branches of industry. To remain competitive in an increasingly global environment and to ensure the safety and reliability of designs, the nuclear industry must take advantage of the new engineering simulation technologies. Concerns surrounding the inappropriate use of simulation by staff without the appropriate competency persist, as analyses become more advanced, increasingly embracing more complex physical phenomena, often in an effort to model reality more faithfully. Furthermore, the age profile of the skilled staff in the nuclear sector in the UK is such that the skills shortage is likely to increase in future. These trends emphasize the need for life-long learning and continual staff development along with transfer of skills from other industry sectors to the nuclear sector. The nuclear industry has taken some initiatives to address skill shortages through the National Skills Academy for Nuclear and Nuclear Energy Skills Alliance (NESA) but these are mostly focused on manufacturing and R&D skills. The recently completed EU funded EASIT2 project is directly aimed at addressing the engineering analysis and simulation skills. This paper gives a brief overview of the EASIT2 project and its deliverables and points out how it can help the skills issues being faced by the nuclear industry. INTRODUCTIO

Adaptive Normalized Risk-Averting Training For Deep Neural Networks

This paper proposes a set of new error criteria and learning approaches, Adaptive Normalized Risk-Averting Training (ANRAT), to attack the non-convex optimization problem in training deep neural networks (DNNs). Theoretically, we demonstrate its effectiveness on global and local convexity lower-bounded by the standard $L_p$-norm error. By analyzing the gradient on the convexity index $\lambda$, we explain the reason why to learn $\lambda$ adaptively using gradient descent works. In practice, we show how this method improves training of deep neural networks to solve visual recognition tasks on the MNIST and CIFAR-10 datasets. Without using pretraining or other tricks, we obtain results comparable or superior to those reported in recent literature on the same tasks using standard ConvNets + MSE/cross entropy. Performance on deep/shallow multilayer perceptrons and Denoised Auto-encoders is also explored. ANRAT can be combined with other quasi-Newton training methods, innovative network variants, regularization techniques and other specific tricks in DNNs. Other than unsupervised pretraining, it provides a new perspective to address the non-convex optimization problem in DNNs.Comment: AAAI 2016, 0.39%~0.4% ER on MNIST with single 32-32-256-10 ConvNets, code available at https://github.com/cauchyturing/ANRA