Increasing the efficiency of model-based industrial processes requires to improve their uncertainty quantification and numerical optimization. Such issues appear in most of the engineering domains (e.g. energy, transport, agriculture) and scientific fields (e.g. biology, high-energy physics). A major problem comes from the black-box nature of the process/function of interest that is often not directly accessible: in general, the only available information are the outputs of the black-box simulation workflow. In particular, derivative information, which is very valuable in the context of optimization and uncertainty quantification, does not exist or is not available. This is a direct consequence of the increasing complexity and diversity of the industrial problems to be addressed (e.g. coupling of multi-physics or multidisciplinary simulators, creation of economic models, working with more sophisticated machine learning models, handling of uncertainty and non-Euclidean variables). Solving this problem is therefore a major issue with direct and significant industrial benefits. In the two last decades, the field of black-box optimization (BBO) methods – especially, derivative-free optimization and surrogate or metamodel (MM) management frameworks – has experienced major theoretical and practical developments. Nevertheless, despite the growing popularity of these methods, some fundamental limitations remain: in particular, the scale of the problems that can currently be efficiently solved by BBO methods does not exceed a few tens of variables and methods to deal with high-dimensional or categorical variables are limited. In real-world applications, the simulation budget is often very restricted. Moreover, BBO algorithms have become complex tools in themselves, which raises questions about their generality of use (choice of kernels for MM) and the reliability of hyper-parameter learning. The main objectives of the project are, jointly, to develop innovative simulation and surrogate-based optimization methodologies, while pushing back their current limits of performance and applicability, guided by real-world applications. These applications are related to the design and risk assessment of critical and complex systems. Hence, the partners of the project will provide challenging and critical applications in the fields of renewable and low-carbon energies and reduced CO2 air transport domains, in order to demonstrate the relevance and the efficiency of the developed methodologies. More precisely, the partners’ ambition is to solve the four following major challenges: - design MM adapted to large scale problems (typically around 100 input variables) in the context of a limited budget of simulations (around 500); - adapt sequential enrichment strategies to large scale problems for reliability-based design optimization and reliability-based inversion purposes; - design efficient black-box optimization methods capable of handling problems mixing input variables of different types: continuous, ordinal and nominal variables; - increase the performance of the iterative process (optimization and MM building) in case of instabilities, failures or non-physical results of the simulation workflow: the aim will be to learn the hidden constraints and deal with them in the adaptive design procedure. Thus, the project aims at (i) consolidating and extending the existing surrogate-based optimization methods to provide a real improvement of their application to industrial problems, (ii) sharing experiences and methodologies of industrial partners for practical problems, (iii) integrating the resulting methods and methodologies in open-source platforms developed by the partners.