This proposal is concerned with the development of novel methodologies (including identification and validation strategies), stochastic representations and numerical methods in stochastic micromechanical modeling of nonlinear microstructures and imperfect interfaces. For the sake of feasibility, the applications will specifically focus on the modeling of hyperelastic microstructures and materials exhibiting surface effects and containing nano-inhomogeneities (such as nanoreinforcements and nanopores). For the case of nonlinear microstructures, the project aims at developing relevant probabilistic models for quantities of interests at both the microscale and mesoscale. The consideration of the latter turns out to be especially suitable for random nonlinear microstructures (such as living tissues) for which the scale separation, which is usually assumed in nonlinear homogenization, cannot be stated. Random variable and random field models for strain-energy functions will be constructed by invoking the maximum entropy principle and propagated through stochastic nonlinear homogenization techniques. A complete methodology for identifying the proposed representations will be further introduced and validated on a simulated database. Concerning the imperfect interface modeling, one may note that surface effects are usually taken into account by retaining an interface model (such as the widely used membrane-type model) involving several assumptions such as those related to the mechanical description of the membrane. Such arbitrary choices certainly generate model uncertainties which may be critical while propagated to coarsest scales and which may therefore penalize the predictive capabilities of the associated multiscale approaches. In this project, we propose to tackle the issue of model uncertainties in multiscale analysis of random microstructures with nano-heterogeneities by constructing nonparametric probabilistic representations for the homogenized properties. A complementary aspect is the construction of robust random generators, able to simulate random variables taking their values in given subspaces defined by inequality constraints and non-Gaussian random fields. Whereas such random fields can typically be generated making use of point-wise polynomial chaos expansions, the preservation of the statistical dependence is hardly achievable with the currently available techniques. In this proposal, we will subsequently address the construction of new random generators relying on the definition of families of Itô stochastic differential equations. Such generators are intended to depend on a limited number of parameters (independent of the probabilistic dimension), for which tuning guidelines will be provided. The proposed models will clearly go a step beyond what is currently done in deterministic mechanics for such materials and the expected results are in the forefront of the ongoing developments within the scopes of uncertainty quantification and material science. In addition, it worth pointing out that such theoretical derivations are absolutely required in order to support the current new developments of 3D-fields measurements and image processing at the microscale of complex materials.

This research intends to study the response of different organizations to the Covid-19 pandemic, comparing the period from March to May 2020 with the period beginning in October 2020. It will focus on three sets of organizations: 1) Government and central administrations, 2) regional and local institutions, 3) the (socio-)health sector. The interviews conducted will be organized around three thematic entries that will help us to better understand the relationships between the different actors and organizations: 1) protection and prevention measures (masks, lock-down, curfew, isolation and physical distancing) ; 2) the organization of tests and screening (availability and choice of tests, contact cases, applications); 3) the management of patients and populations at risk (in hospital, at home, respiratory equipment, treatments, transportation). This research aims to uncover and analyse the capacities of these organisations to transform themselves or not in a period of uncertainty, by favoring an approach centered on collective action (to analyse the forms of cooperation or conflict that arise during crisis management) and a cognitive approach (which looks at the way in which actors make sense of the crisis and legitimize their actions). By comparing two periods, we will seek to see whether the capacities for cooperation differ according to whether the situation is marked by a high degree of uncertainty, urgency and extraordinary functioning; or, on the contrary, a better knowledge of the risks, less time pressure and a return to ordinary functioning. The goal will be to produce, in addition to fundamental knowledge about organizations in crisis situations, an analysis shared with the actors involved in the management of the crisis in these different organizations, with a view to collective learning.

DEPOSIA focuses on the detection and geolocation of various radio frequency signal sources in order to thwart attacks on connected systems and infrastructures. The sources considered are elements which by their characteristics or their position, present an illicit character and which threaten the people security or the infrastructures. For outdoor cases, we consider drones flying over forbidden areas, telecommunication jammers, spoofing signal transmitters or wireless connected sensors used to introduce false data in monitoring platforms. For indoor cases, we also consider jamming or spoofing sources that can cause denial of service within networks or infrastructures, or fake access points that aim to carry out man-in-the-middle attacks to intercept information. In this proposal, the indoor and outdoor use cases are considered separately in order to design monitoring infrastructures adapted to each case. For the outdoor case, we consider a surveillance architecture that could join the already existing cellular or WLAN communication infrastructures. In particular, with 5G technology and the higher employed frequencies, cellular networks are evolving towards finer meshes and have interfaces with the core network at each of their nodes. Thus, these interface points, equipped with receivers dedicated to monitoring, could enable the routing of monitoring data to centralized platforms, feeding an Artificial Intelligence for analysis, anomaly detection and source geolocation. For the indoor case, we consider a distributed monitoring architecture deployed within a building, based on SDR sensors and a data centralization and synchronization network. In these two cases, we envisage an Artificial Intelligence working on data evolving in three dimensions : time, space and direction, all for data of different natures, namely those from the physical layer and the data link layer. Whether for indoor or outdoor configurations, the algorithms that will constitute the Artificial Intelligence will be based on learning approaches that will correspond to Machine Learning and Deep Learning algorithms. These algorithms will deal with the problems of detecting attacks and locating illicit sources. These algorithms will have to take into account: the evolutionary aspect brought by the non-fixed character in time of the attacks and the non-fixed location aspect of the localization of the source of the attack. A first Artificial Intelligence will be dedicated to data analysis and anomaly detection, i.e., highlighting the suspicious nature of the data, and a second Artificial Intelligence will be dedicated to extracting the location information of the attack source. Due to the multi-layered nature of the data, model aggregation algorithms will be deployed in order to homogenize the decision process.

Computer-based modelling and simulations are fast entering the realm of governance. They are proving to be a necessary expertise and type of knowledge to use in public decision-making. To be sure, experts advising governments in various areas have long been using models and modeling techniques, in the simple sense of the creation of systems of elements that are logically or formally related to one another. Crime and social unrest, toxicity, energy production and consumption or climate have all been modeled by various scientific communities, and those models have been fed in national and international policy-making. IT technologies, however, are fast increasing the capacity to model and, through the use of models, to simulate, visualize, anticipate, build scenarios or predict. In this sense, modeling and simulations represent technical innovations in governance and public action. The INNOX project has established three objectives. The first is to explain how governance has become a space that is prone to technological innovation, and specifically to modeling as an innovation. Second, the project aims to assess the extent of the changes that modeling really creates in governance, in terms of a change in the types of actors involved in governance and relationships between them, as well as the practices and types of knowledge used in administrative decision-making and public intervention or the ways of conceptualizing and speaking about public issues and policies. These objectives will be achieved by means of investigation in three different types of modeling or use of models in governance: model-based simulation in the area of crime control; scenarios of energy transition; predictive and high-scale evaluation of chemicals toxicity. Three different lines of inquiry will be followed in the investigation of these cases. The first theme is “production and markets for models”, or the proponents and developers of models and their uses: expert communities and particular industries or companies. It will also look into the competition between those very actors and their relationships with public authorities. The second theme is “Administration and decision”. It looks at the shifts that occur (or not) in the practices of decision-making in agencies, and related evolutions in the types of knowledge and expertise used, as well as modes of organization and relationships of public organizations with their audiences and interest groups. Third, an analysis of “public space, mobilizations and controversies” will be undertaken, to assess how and to what extent models produce a new form of knowledge and arguments for social movements or public interest groups, and how that impacts on dominant policy paradigms. Documentary, qualitative and quantitative analyses will be used for each line of inquiry in each of the three cases. To achieve the project, a team of 9 researchers from two closely related social science research centres has been put together. Both centres work in the areas of science and technology studies, as well as research and innovation policies. They are part of the Institut Francilien Recherche Innovation Société. There will be a close interaction throughout the project with organizations that have a stake in the development of modeling in each of the three cases or sectors. These organizations are included as partners in the project. The expected benefits of carrying out this ambitious project are: an improvement of the understanding of current technological transitions in governance and expertise; an increase in the capacity of the very actors of these transitions to make sense of it and steer it; the development of an analytical scheme for innovations in expert-based governance; finally, the successful installation of the team of social scientists and of the two social science centres as reference for research in the nascent field of studies of Science, Technology and Innovation in Society.