Physics dictate that a flow device has to leave a wake or the signature of it producing sustentation forces, extracting energy, or simply moving through the medium; these flow structures can then impact negatively or favorably another device downstream. Wake turbulence between aircraft in air traffic and wake losses within wind farms are prime examples of this phenomenon, and incidentally constitute pivotal challenges to their respective fields of transportation and wind energy. These are highly complex and unsteady flows, and distributed control based on affordable wake models has failed to produce robust schemes that can alleviate turbulence effects and achieve efficiency at the scale of the system of devices. This project proposes an Artificial Intelligence and bio-inspired paradigm for the control of flow devices subjected to wake effects. To each flow device, we associate an intelligent agent that pursues given goals of efficiency or turbulence alleviation. Every one of these flow agents now relies on machine-learning tools to learn how to make the right decision when confronted with wake or turbulent flow structures. At a system level, we employ Multi-Agent System and Distributed Learning paradigms. Based on Game Theory, we build a system of interactions that incite the emergence of collaborative behaviors between the agents and achieve global optimized operation among the devices. We claim that the design of a system that learns how to control the flow, is simpler than the design of the control scheme and will yield a more robust scheme. The learning of formation flying among aircraft and of wake alleviation between wind turbines will constitute our study cases. The investigation will essentially be carried by means of large-scale numerical simulations; such simulations will produce the first ever realizations of self-organized systems in a turbulent flow. We will then apply our learning frameworks to a small-scale wind farm.

Symmetric & asymmetric cryptography offer the basic functionalities needed to communicate securely over a channel. Due to their different features and the different algebraic structures they exploit, the interaction between the design of these primitives and the security of their implementation against side-channel & fault attacks so far followed somewhat separated paths. Based on the observation that (i) many emerging challenges for the implementation security of symmetric & asymmetric primitives share similarities and would highly benefit from a more connected approach, and (ii) this is especially true when considering post-quantum asymmetric encryption schemes that include symmetric components and for which current designs are extremely challenging to protect against side-channel & faults attacks, the BRIDGE project aims to develop a unified treatment of symmetric & asymmetric cryptography by leveraging three innovative movements. First, we aim to export the concept of levelled implementation (where different parts of a primitive are protected with countermeasures of varying cost) from symmetric cryptography towards new post-quantum asymmetric schemes that inherently take implementation security as a design criteria. Second, we aim to export the use of larger (possibly prime) fields and more complex algebraic structures used in asymmetric cryptography to deliver advanced functionalities towards new symmetric schemes that guarantee security against side-channel & fault attacks in low-noise contexts that raise fundamental challenges for existing countermeasures. Third, we aim to exploit hard physical learning problems as radically new building blocks applicable to both types of primitives. By combining these movements, we aim to identify disruptive approaches to build new cryptographic schemes offering a better integration between symmetric & asymmetric designs and improvements of their implementation security by orders of magnitude.