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As next-generation scientific instruments and simulations generate ever larger datasets, there is a growing need for high-performance computing (HPC) techniques that can provide timely and accurate analysis. With artificial intelligence (AI) and hardware breakthroughs at the forefront in recent years, interest in using this technology to perform decision-making tasks with continuously evolving real-world datasets has increased. Digital twinning is one method in which virtual replicas of real-world objects are modeled, updated, and interpreted to perform such tasks. However, the interface between AI techniques, digital twins (DT), and HPC technologies has yet to be thoroughly investigated despite the natural synergies between them. This paper explores the interface between digital twins, scientific computing, and machine learning (ML) by presenting a consistent definition for the digital twin, performing a systematic analysis of the literature to build a taxonomy of ML-enhanced digital twins, and discussing case studies from various scientific domains. We identify several promising future research directions, including hybrid assimilation frameworks and physics-informed techniques for improved accuracy. Through this comprehensive analysis, we aim to highlight both the current state-of-the-art and critical paths forward in this rapidly evolving field. 

Benchmark and system parameters often have a significant impact on performance evaluation, which raises a long-lasting question about which settings we should use. This paper studies the feasibility and benefits of extensive evaluation. A full extensive evaluation, which tests all possible settings, is usually too expensive. This work investigates whether it is possible to sample a subset of the settings and, upon them, generate observations that match those from a full extensive evaluation. Towards this goal, we have explored the incremental sampling approach, which starts by measuring a small subset of random settings, builds a prediction model on these samples using the popular ANOVA approach, adds more samples if the model is not accurate enough, and terminates otherwise. To summarize our findings: 1) Enhancing a research prototype to support extensive evaluation mostly involves changing hard-coded configurations, which does not take much effort. 2) Some systems are highly predictable, which means that they can achieve accurate predictions with a low sampling rate, but some systems are less predictable. 3) We have not found a method that can consistently outperform random sampling + ANOVA. Based on these findings, we provide recommendations to improve artifact predictability and strategies for selecting parameter values during evaluation.