This work presents a framework to predict near-optimal anisotropic spacing functions suitable to perform simulations with unseen operating conditions or geometric configurations. The strategy consists of utilising the vast amount of high-fidelity data available in industry to compute a target anisotropic spacing and train an artificial neural network to predict the spacing for unseen scenarios. The trained neural network outputs the metric tensor at the nodes of a coarse background mesh that is then used to generate meshes for unseen cases. Examples are used to demonstrate the effect of the network hyperparameters and the training dataset on the accuracy of the predictions. The potential of the method is demonstrated for examples involving up to 11 geometric parameters on CFD simulations involving a full aircraft configuration.

Background: Software Package Registries (SPRs) are an integral part of the software supply chain. These collaborative platforms unite contributors, users, and code for streamlined package management. Prior work has characterized the SPRs associated with traditional software, such as NPM (JavaScript) and PyPI (Python). Pre-Trained Model (PTM) Registries are an emerging class of SPR of increasing importance, because they support the deep learning supply chain. A growing body of empirical research has examined PTM registries from various angles, such as vulnerabilities, reuse processes, and evolution. However, no synthesis provides a systematic understanding of current knowledge. Furthermore, much of the existing research includes non-quantified qualitative observations.

The development and training of deep learning models have become increasingly costly and complex. Consequently, software engineers are adopting pre-trained models (PTMs) for their downstream applications. The dynamics of the PTM supply chain remain largely unexplored, signaling a clear need for structured datasets that document not only the metadata but also the subsequent applications of these models. Without such data, the MSR community cannot comprehensively understand the impact of PTM adoption and reuse.

One major goal for data mining is to understand data. Rule based methods are better than other methods in making mining results comprehensible. However, current rule based classifiers make use of a small number of rules and a default prediction to build a concise predictive model. This reduces the explanatory ability of the rule based classifier. In this paper, we propose to use multiple and negative target rules to improve explanatory ability of rule based classifiers. We show experimentally that this understandability is not at the cost of accuracy of rule based classifiers.

The use of Web services as the basis for grid middleware has allowed scientists to wrap legacy applications as services in order to provide their capabilities to grid users. However, this "wrapper" model can be poor at satisfying the requirement for software and data to be efficiently positioned on processing resources around the grid as part of larger coordinated computations. We argue that some aspects of the earlier more exposed grid model, where the client coordinates the use of resources, support these issues better and allow more efficient grid solutions. We propose a model to take advantage of both systems