Data-Gouv-ML/pcri-erc-participations-et-projets-laureats huggingface dataset:
Projet ERC : 321 (Horizon 2020, appel 2016, Consolidator grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Systems and communication engineering
Entité participante : Institut Mines-Télécom — Pays : France — Rôle : Partner
Acronyme de l'entité : IMT
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 0.0 €

Computational Electromagnetics (CEM) is the scientific field at the origin of all new modeling and simulation tools required by the constantly arising design challenges of emerging and future technologies in applied electromagnetics. As in many other technological fields, however, the trend in all emerging technologies in electromagnetic engineering is going towards miniaturized, higher density and multi-scale scenarios. Computationally speaking this translates in the steep increase of the number of degrees of freedom. Given that the design cost (the cost of a multi-right-hand side problem dominated by matrix inversion) can scale as badly as cubically with these degrees of freedom, this fact, as pointed out by many, will sensibly compromise the practical impact of CEM on future and emerging technologies. For this reason, the CEM scientific community has been looking for years for a FFT-like paradigm shift: a dynamic fast direct solver providing a design cost that would scale only linearly with the degrees of freedom. Such a fast solver is considered today a Holy Grail of the discipline. The Grand Challenge of 321 will be to tackle this Holy Grail in Computational Electromagnetics by investigating a dynamic Fast Direct Solver for Maxwell Problems that would run in a linear-instead-of-cubic complexity for an arbitrary number and configuration of degrees of freedom. The failure of all previous attempts will be overcome by a game-changing transformation of the CEM classical problem that will leverage on a recent breakthrough of the PI. Starting from this, the project will investigate an entire new paradigm for impacting algorithms to achieve this grand challenge. The impact of the FFT’s quadratic-to-linear paradigm shift shows how computational complexity reductions can be groundbreaking on applications. The cubic-to-linear paradigm shift, which the 321 project will aim for, will have such a rupturing impact on electromagnetic science and technology.

Mots-clés : fast direct solvers,computational electromagnetics
Projet ERC : EPRICOT (Horizon 2020, appel 2020, Consolidator grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Computer science and informatics
Entité participante : Imperial College London — Pays : Royaume-Uni — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1899675.0 €

Computational complexity lies at the heart of information and computer science. Its aim is to formally understand the boundary between problems that can be solved efficiently and those that cannot. This has many applications: new algorithms are important to make progress in domains such as machine learning and optimization, and new complexity lower bounds (namely, computational impossibility results) are essential to provably secure cryptography. Beyond practical applications, these questions reveal deep mathematical and natural phenomena. One of the prominent directions to attack the fundamental lower bound questions in complexity comes from the study of resource bounded provability, namely proof complexity. Its aim is to understand which problems possess solutions with short correctness proofs and which do not. Traditionally, proof complexity is concerned with propositional (Boolean) logic, and thus techniques from Boolean function complexity have had a huge impact on the field, driving many of its results and agendas. In this proposal we suggest to employ recent breakthroughs in the field of proof complexity exploiting algebraic approaches to broaden in a systematic way both the scope and techniques of proof complexity, going from weak settings to the very strong ones, up to the major open problems in the field and beyond. In particular, we propose to use algebraic notions and techniques such as structural algebraic circuit complexity, rank lower bounds, noncommutative and PI algebras, among others to significantly broaden the arsenal of lower bound tools as well as develop new models and insights into proofs, computation and their inter-relations. This project has potential for a transformative impact in theoretical computer science and beyond, with applications to unconditional computational lower bounds, which underlie secure cryptography and derandomization of probabilistic algorithms, as well as improved SAT- and constraint-solving heuristics.

Mots-clés : computational complexity,propositional proof systems,lower bounds,impossibility results,theory of computing,bounded arithmetic,satisfiability,algebraic computing,algebraic circuits,boolean circuits,complexity,proof complexity
Projet ERC : DISCOVERER (Horizon 2020, appel 2020, Proof of concept grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Physical and analytical chemical sciences
Entité participante : University of Luxembourg — Pays : Luxembourg — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 150000.0 €

Computational design and discovery of molecules and materials relies on the exploration of increasingly growing chemical spaces. The discovery and formulation of new drugs, antivirals, antibiotics, catalysts, battery materials, and in general chemicals with tailored properties, require a fundamental paradigm shift to search in unchartered swaths of the vast chemical space. This is in stark contrast to current approaches, which start from (commercially available) libraries of compounds from various suppliers. Within the ERC Consolidator grant BeStMo (grant agreement ID 725291) we aimed to substantially advance our ability to model and understand the behaviour of molecules in complex environments. As a result, we successfully developed a set of machine learning and physics-based methods for covalent and non-covalent interactions that now allow an accurate and efficient modelling of molecules of increasing size (from 10 to 1000 atoms). These methods now enable routine calculations of quantum-mechanical properties of molecules throughout chemical compound space, provided that enough reference data is produced as a starting point for training. Within DISCOVERER, we aim to promote a paradigm shift in chemical discovery by inverting the selection pyramid by starting with pre-defined parameters from which new chemical entities are designed through machine learning and AI-enabled algorithms. We can do so by integrating these modules into a commercial platform: “Chemical Space Machine”. DISCOVERER’s main goal is to finalize the development of a commercial alpha version of “Chemical Space Machine” and setting up its commercialisation strategy.

Mots-clés : chemical space,chemoinformatics,machine learning,new chemical entities
Projet ERC : Big Splash (Horizon 2020, appel 2014, Starting grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Computer science and informatics
Entité participante : Institute of Science and Technology Austria — Pays : Autriche — Rôle : PI
Acronyme de l'entité : ISTA
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1500000.0 €

Computational simulations of natural phenomena are essential in science, engineering, product design, architecture, and computer graphics applications. However, despite progress in numerical algorithms and computational power, it is still unfeasible to compute detailed simulations at large scales. To make matters worse, important phenomena like turbulent splashing liquids and fracturing solids rely on delicate coupling between small-scale details and large-scale behavior. Brute-force computation of such phenomena is intractable, and current adaptive techniques are too fragile, too costly, or too crude to capture subtle instabilities at small scales. Increases in computational power and parallel algorithms will improve the situation, but progress will only be incremental until we address the problem at its source. I propose two main approaches to this problem of efficiently simulating large-scale liquid and solid dynamics. My first avenue of research combines numerics and shape: I will investigate a careful de-coupling of dynamics from geometry, allowing essential shape details to be preserved and retrieved without wasting computation. I will also develop methods for merging small-scale analytical solutions with large-scale numerical algorithms. (These ideas show particular promise for phenomena like splashing liquids and fracturing solids, whose small-scale behaviors are poorly captured by standard finite element methods.) My second main research direction is the manipulation of large-scale simulation data: Given the redundant and parallel nature of physics computation, we will drastically speed up computation with novel dimension reduction and data compression approaches. We can also minimize unnecessary computation by re-using existing simulation data. The novel approaches resulting from this work will undoubtedly synergize to enable the simulation and understanding of complicated natural and biological processes that are presently unfeasible to compute.

Mots-clés : computer animation,physics simulation,fracturing solids,liquid simulation,computational fluid dynamics,animation control
Projet ERC : eDrug (Horizon 2020, appel 2014, Proof of concept grants)
Entité participante : Barcelona Supercomputing Center — Pays : Espagne — Rôle : PI
Acronyme de l'entité : BSC
Type d'entité : Recherche
Financement ERC de l'entité : 148875.0 €

Computer simulations are taking over in design processes. Nevertheless, complex projects will largely benefit from human intuition and team-work. We aim to combine computer and human power into the next generation of fully interactive computational tools for drug design. The goal of eDRUG is prove the potential of this novel computational solution in the current pipeline of drug development and the prospective interest that it will raise in the pharmaceutical industry. We aim at merging human knowledge with state of the art affordable computational resources to create an interactive and (capable of) intuition-guided software solution. The idea seats on three technological pillars: 1) the PELE software allowing fast biophysical characterization of drug-target interactions, 2) advances in computational hardware providing more computational performance in reduced space, requiring lower power consumption and acquisition cost, and 3) better graphical devices allowing a richer and faster human(team) data analysis and software interaction. The goal matches with increasing and future needs from pharmaceutical companies to improve rational drug design (in response to cell/virus mutations, personalized medicine, etc.). One worldwide leading pharmaceutical company, and a local biotech SME, will be counseling the developing team and testing the prototypes, to meet a product that fulfills industry expectations.

Mots-clés : pele,drug design,hardware,software solution,supercomputing
Projet ERC : FIAMMA (Horizon 2020, appel 2020, Consolidator grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Physical and analytical chemical sciences
Entité participante : Board of the Swiss Federal Institutes of Technology — Pays : Suisse — Rôle : PI
Acronyme de l'entité : ETH Zurich
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1998015.0 €

Computer simulations of molecules and materials are undergoing a profound transformation. Machine learning (ML) has become essential to extend the reach and increase the predictive power of atomic-scale modeling. The potential of ML in association with quantum mechanical (QM) and statistical mechanical (SM) methods has been shown, but the link has been rather superficial, due to the complex, interdisciplinary effort needed to combine the three approaches. Without full convergence, ML-powered simulations cannot address modern modeling challenges, which involve complex materials in realistic conditions, and require increasingly predictive accuracy. The objective of this project is to create a “plug and play” framework by which ML can be seamlessly combined with physics-based modeling, substituting individual steps of a QM calculation, or making direct predictions across complex SM workflows. Full integration of the three approaches will greatly extend the reach of atomistic simulations, and allow an insightful critical comparison of the role of inductive and deductive paradigms in theory and modeling. The development of an open-source software that unifies QM, SM and ML shall facilitate early adoption and broaden impact. We will demonstrate the benefits of our integrated framework through two challenging and compelling platform problems: (i) investigating stabilities and properties of flexible drug-like molecules and assemblies, and (ii) discovering fundamental structure-activity relationships of porous aluminosilicates for clean chemical technologies. Critical knowledge gaps that will be filled include: (1) the description of long-range physics within the same conceptual framework that has been used for short-range interactions; (2) the symmetry-adapted representation of input and outputs of each step of a QM calculation; and (3) the rigorous characterization of SM ensembles to enable end-to-end predictions of equilibrium properties with uncertainty quantification.

Mots-clés : atomistic modeling,machine learning,molecular dynamics,molecular materials,zeolites,catalysis
Projet ERC : VESTA (Horizon 2020, appel 2017, Consolidator grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Computer science and informatics
Entité participante : Université de Rennes — Pays : France — Rôle : Partner
Acronyme de l'entité : UNIVRENNES
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 236065.0 €

Computer software pervades our life but far too much of it contains programming errors (bugs). Software is more and more complex and such errors are unavoidable if programmers are not accompanied with some tools that help auditing software codes. Static analysis is an increasingly popular technique that aims at automatically compute properties of software. These properties then help finding bugs, or proving absence of them. Industrial static analysers are flourishing. Facebook, Google, Microsoft develop their own static analysis tools to help maintaining their huge code base. Critical software industry (aircraft, railways, nuclear, etc.) has embraced the use of advanced static analysis tool as Astrée to companion and sometimes, ligthen their traditional software validation campaigns based on meticulous testing and reviews. Unfortunately, designing advanced static analyses like Astrée requires a very rare expertise in Abstract Interpretation, a foundational landmark in the research area, and implementing these ideas efficiently and correctly is specially tricky. The VESTA project will propose guidance and tool-support to the designers of static analysis, in order to build advanced but reliable static analysis tools. We focus on analyzing low-level softwares written in C, leveraging on the CompCert verified compiler. This compiler toolchain is fully verified in the Coq proof assistant. Verasco is a verified static analyser that I have architected. It analyses C programs and follows many of the advanced abstract interpretation technique developped for Astrée, but it is formally verified. The outcome of the VESTA project will be a platform that help designing other verified advanced abstract interpreters like Verasco, without starting from a white page. We will apply this technique to develop security analyses for C programs. The platform will be open-source and will help the adoption of abstract interpretation techniques.

Mots-clés : program verification,proof assistant,semantics,programming langages
Projet ERC : SuPerCom (Horizon 2020, appel 2017, Consolidator grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Computer science and informatics
Entité participante : Barcelona Supercomputing Center — Pays : Espagne — Rôle : PI
Acronyme de l'entité : BSC
Type d'entité : Recherche
Financement ERC de l'entité : 1998918.75 €

Computers increasingly intervene in critical aspects of our life related to health, safety, and security, resulting in (critical) software controlling functionalities or services with humans in the loop. This trend towards critical-function digitization brings huge benefits for society and rests two pillars: the use of high-performance parallel hardware as the only viable option to cover the highest-ever critical software’s performance needs; and the ability to provide sustainable (guaranteed) performance, instead of average unreliable performance. Failing to support both pillars prevents embedded computers from safely executing critical software potentially causing unacceptable risks or threats to human life. SuPerCom goes beyond current solutions, which face either major scalability limitations or cannot provide performance guarantees, and proposes a holistic multidisciplinary approach that addresses the challenge of providing high and sustainable performance with future embedded computers comprising high-performance hardware with unprecedented complexity levels. SuPerCom synergistically combines for the first time performance analysis, hardware design and statistical and machine learning techniques. With SuPerCom performance predictability and performance observability become first-class citizen hardware requirements, rather than being considered at the end of the design. SuPerCom also proposes statistical and machine-learning techniques to (i) deal with big amounts of performance data coming from hardware sensors and (ii) provide on-line optimizations to increase sustainable performance. SuPerCom breakthrough can have significant economic and societal impact by allowing embedded computers to use high-performance hardware with strong guarantees of sustainable performance. This, in turn, will allow executing a wide-variety of performance-demanding critical software like advanced driver assistance systems in cars or advanced medical devices with sound guarantees.

Mots-clés : performance predictability,embedded computing systems,processor architecture
Projet ERC : ArtsAutonomy (Horizon 2020, appel 2019, Starting grants)
Domaine scientifique : Social Sciences and Humanities
Panel : Cultures and cultural production
Entité participante : Ludwig-Maximilians-Universität München — Pays : Allemagne — Rôle : PI
Acronyme de l'entité : LMU
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 1229665.0 €

Concise, massively ideological texts have long been perceived as constitutive of the development and transformation of opinion and policy in the public sphere. At least since the early Enlightenment, the pamphlet has been at the forefront of major philosophical, social, and political transformations, including processes of democratization and dedemocratization, colonization and decolonization, the universalization of civil and civic rights, the enforcement of political or territorial autonomy, and the critique of labor exploitation. Since the fall of the Berlin Wall, pamphleteering has reemerged as a vital transformative and polemical force in liberal Western societies, prompting often polarized discussions of contentious issues. By integrating philological, historical, and politological research perspectives, ArtsAutonomy will produce a first systematic, large-scale account of contemporary pamphleteering and shed new light on the perceived ongoing radicalization of political, cultural, and social ideals and discourses in contemporary Europe and the United States. We intend to achieve our epistemic goals by studying “pamphletary events”, i.e. events which unfold in the public sphere and which engage both a pamphletary text and interpretative competences widely distributed among the general reading public. ArtsAutonomy will produce several thematically organized case studies of pamphletary events, including analytic “slices” of evolving public opinion on pamphletary statements, as recorded in transcripts of public deliberations, open letters, online commentary, user and consumer feedback, and social media. Our multidisciplinary approach will provide a unique insight into the concrete current political agency of one of the oldest literary forms, as well as into the philological-interpretative competences upon which pamphletary events and their normalization are predicated.

Mots-clés : pamphleteering,philology,polemic,virality,policy,civic turn
Projet ERC : EXOCONDENSE (Horizon 2020, appel 2016, Advanced grants)
Domaine scientifique : Physical Sciences and Engineering
Panel : Universe sciences
Entité participante : University of Oxford — Pays : Royaume-Uni — Rôle : PI
Type d'entité : Enseignement supérieur ou secondaire
Financement ERC de l'entité : 2492565.0 €

Condensible substances, which undergo a phase change from gaseous to liquid or solid condensed form, have a profound impact on planetary atmospheres, and are central to the determination of most key aspects of a planet's climate. The three phases of water operating in Earth's present climate provide the archetype for condensible processes in climate dynamics, but the dawning age of exoplanet discovery and characterization requires that the understanding of phase change effects be expanded far beyond the situations familiar from the study of Earth's climate, or indeed of the climate of any Solar System planet. The goal of this project is to pioneer the advances needed to understand condensible climate dynamics for the vastly broader range of condensible substances, thermodynamic and planetary configurations presented by the growing catalogue of exoplanets. The emphasis will be on the smaller range of planets (super-Earth to Earth mass or size class), which need not have hydrogen-dominated atmospheres and therefore present a richer and highly challenging variety of possible condensible behavior. This class of planets includes all planets that are potentially habitable for Earthlike life, but even planets that are far from habitable shed light on essential features of planets in the Universe, and will be studied. The work will embrace both small scale buoyancy-driven turbulent convection and planetary scale circulations. Idealized numerical simulations, buttressed by theoretical analysis will be employed. Particular emphasis will be put on aspects of exoclimate that are amenable to probing by current observations and improved observational techniques likely to become available in the coming decade. Such properties include cloud properties observable through transit-depth spectra and dayside/nightside temperature and composition contrasts observable through phase curve observations.

Mots-clés : exoplanets,phase change,climate simulation,exoclimate