Discrete response of structures is often a key probabilistic quantity of interest. For example, one may need to identify the probability of a binary event, such as, whether a structure has buckled or not. In this study, an adaptive domain-based decomposition and classification method, combined with sparse grid sampling, is used to develop an efficient classification surrogate modeling algorithm for such discrete outputs. An assumption of monotonic behaviour of the output with respect to all model parameters, based on the physics of the problem, helps to reduce the number of model evaluations and makes the algorithm more efficient. As an application problem, this paper deals with the development of a computational framework for generation of probabilistic penetration response of S-2 glass/SC-15 epoxy composite plates under ballistic impact. This enables the computationally feasible generation of the probabilistic velocity response (PVR) curve or the $V_0-V_{100}$ curve as a function of the impact velocity, and the ballistic limit velocity prediction as a function of the model parameters. The PVR curve incorporates the variability of the model input parameters and describes the probability of penetration of the plate as a function of impact velocity.

Mistrust in artificial intelligence (AI) is a curious thing; it's truly indicative of human nature. With machine learning (ML) activated, if a computer makes a mistake, it will never make that mistake again, yet it is often perceived as a broken tool. Without said intervention, humans can make the same mistake innumerable times, either without coming under scrutiny or before being replaced by a different human with the same ingrained deficiencies. One error can be called a fault in the system, yet the financial and logistical implications that numerous "one-time errors" can have on a retail operation are huge. Larger chains and organizations may need to make tens of millions of decisions each day regarding stock, price points, product placements and even marketing activities.

People always talk about artificial intelligence (AI), but they remain reluctant to dig into what it means or how it can implicate their businesses. Enterprises need to go beyond a basic understanding and learn how AI can unearth what they don't know about their own company. For a retailer, decisions regarding stock levels, price points, distribution volumes or even promotions all fall under this "what you don't know" umbrella. Most decisions are largely made by gut feeling -- an individual who believes their experience will always outperform a computer. When unhindered by human interference or pride, AI and machine learning (ML) will instead ensure that all these decisions are made off the back of statistics and data pulled from every action taking place within a retailer's stores.

In November of 2012, Jan Scheuermann did something she never thought she would do again: She fed herself a piece of chocolate. For the last decade Scheuermann, 54, has been a prisoner in her own body. She suffers from a mysterious degenerative disorder that attacks the nervous system, severing the connections between the brain and muscles. Now a quadriplegic, Scheuermann has no movement below her neck. She can't move her limbs, let alone grasp, move, or hold anything.

The emergence of mobile games has caused a paradigm shift in the video-game industry. Game developers now have at their disposal a plethora of information on their players, and thus can take advantage of reliable models that can accurately predict player behavior and scale to huge datasets. Churn prediction, a challenge common to a variety of sectors, is particularly relevant for the mobile game industry, as player retention is crucial for the successful monetization of a game. In this article, we present an approach to predicting game abandon based on survival ensembles. Our method provides accurate predictions on both the level at which each player will leave the game and their accumulated playtime until that moment. Further, it is robust to different data distributions and applicable to a wide range of response variables, while also allowing for efficient parallelization of the algorithm. This makes our model well suited to perform real-time analyses of churners, even for games with millions of daily active users.