ERC FUNDED PROJECTS

Space exploration is one of boldest and most exciting endeavors that humanity has undertaken, and it holds enormous promise for the future. Our next challenges for the spatial conquest include bringing back samples to Earth by means of robotic missions and continuing the manned exploration program, which aims at sending human beings to Mars and bring them home safely. Inaccurate prediction of the heat-flux to the surface of the spacecraft heat shield can be fatal for the crew or the success of a robotic mission. This quantity is estimated during the design phase. An accurate prediction is a particularly complex task, regarding modelling of the following phenomena that are potential “mission killers:” 1) Radiation of the plasma in the shock layer, 2) Complex surface chemistry on the thermal protection material, 3) Flow transition from laminar to turbulent. Our poor understanding of the coupled mechanisms of radiation, ablation, and transition leads to the difficulties in flux prediction. To avoid failure and ensure safety of the astronauts and payload, engineers resort to “safety factors” to determine the thickness of the heat shield, at the expense of the mass of embarked payload. Thinking out of the box and basic research are thus necessary for advancements of the models that will better define the environment and requirements for the design and safe operation of tomorrow’s space vehicles and planetary probes for the manned space exploration. The three basic ingredients for predictive science are: 1) Physico-chemical models, 2) Computational methods, 3) Experimental data. We propose to follow a complementary approach for prediction. The proposed research aims at: “Integrating new advanced physico-chemical models and computational methods, based on a multidisciplinary approach developed together with physicists, chemists, and applied mathematicians, to create a top-notch multiphysics and multiscale numerical platform for simulations of planetary atmosphere entries, crucial to the new challenges of the manned space exploration program. Experimental data will also be used for validation, following state-of-the-art uncertainty quantification methods.”

1 494 892 €

Start date: 2010-09-01, End date: 2015-08-31

Computer animation has traditionally been associated with applications in virtual-reality-based training, video games or feature films. However, interactive animation is gaining relevance in a more general scope, as a tool for early-stage analysis, design and planning in many applications in science and engineering. The user can get quick and visual feedback of the results, and then proceed by refining the experiments or designs. Potential applications include nanodesign, e-commerce or tactile telecommunication, but they also reach as far as, e.g., the analysis of ecological, climate, biological or physiological processes. The application of computer animation is extremely limited in comparison to its potential outreach due to a trade-off between accuracy and computational efficiency. Such trade-off is induced by inherent complexity sources such as nonlinear or anisotropic behaviors, heterogeneous properties, or high dynamic ranges of effects. The Animetrics project proposes a modeling and animation methodology, which consists of a multi-scale decomposition of complex processes, the description of the process at each scale through combination of simple local models, and fitting the parameters of those local models using large amounts of data from example effects. The modeling and animation methodology will be explored on specific problems arising in complex mechanical phenomena, including viscoelasticity of solids and thin shells, multi-body contact, granular and liquid flow, and fracture of solids.

1 277 969 €

Start date: 2012-01-01, End date: 2016-12-31

For a large family of computational problems collectively known as constrained optimization and satisfaction problems (CSPs), four decades of research in algorithms and computational complexity have led to a theory that tries to classify them as algorithmically tractable vs. intractable, i.e. polynomial-time solvable vs. NP-hard. However, there remains an important gap in our knowledge in that many CSPs of interest resist classification by this theory. Some such problems of practical relevance include fundamental partition problems in graph theory, isomorphism problems in combinatorics, and strategy-design problems in mathematical game theory. To tackle this gap in our knowledge, the research of the last decade has been driven either by finding hard instances for algorithms that solve tighter and tighter relaxations of the original problem, or by formulating new hardness-hypotheses that are stronger but admittedly less robust than NP-hardness. The ultimate goal of this project is closing the gap between the partial progress that these approaches represent and the original classification project into tractable vs. intractable problems. Our thesis is that the field has reached a point where, in many cases of interest, the analysis of the current candidate algorithms that appear to solve all instances could suffice to classify the problem one way or the other, without the need for alternative hardness-hypotheses. The novelty in our approach is a program to develop our recent discovery that, in some cases of interest, two methods from different areas match in strength: indistinguishability pebble games from mathematical logic, and hierarchies of convex relaxations from mathematical programming. Thus, we aim at making significant advances in the status of important algorithmic problems by looking for a general theory that unifies and goes beyond the current understanding of its components.

1 725 656 €

Start date: 2015-06-01, End date: 2020-05-31

"Tissue engineering (TE), the interdisciplinary field combining biomedical and engineering sciences in the search for functional man-made organ replacements, has key issues with the quantity and quality of the generated products. Protocols followed in the lab are mainly trial and error based, requiring a huge amount of manual interventions and lacking clear early time-point quality criteria to guide the process. As a result, these processes are very hard to scale up to industrial production levels. BRIDGE aims to fortify the engineering aspects of the TE field by adding a higher level of understanding and control to the manufacturing process (MP) through the use of in silico models. BRIDGE will focus on the bone TE field to provide proof of concept for its in silico approach. The combination of the applicant's well-received published and ongoing work on a wide range of modelling tools in the bone field combined with the state-of-the-art experimental techniques present in the TE lab of the additional participant allows envisaging following innovation and impact: 1. proof-of-concept of the use of an in silico blue-print for the design and control of a robust modular TE MP; 2. model-derived optimised culture conditions for patient derived cell populations increasing modular robustness of in vitro chondrogenesis/endochondral ossification; 3. in silico identification of a limited set of in vitro biomarkers that is predictive of the in vivo outcome; 4. model-derived optimised culture conditions increasing quantity and quality of the in vivo outcome of the TE MP; 5. incorporation of congenital defects in the in silico MP design, constituting a further validation of BRIDGE’s in silico approach and a necessary step towards personalised medical care. We believe that the systematic – and unprecedented – integration of (bone) TE and mathematical modelling, as proposed in BRIDGE, is required to come to a rationalized, engineering approach to design and control bone TE MPs."

1 191 440 €

Start date: 2011-12-01, End date: 2016-11-30