Guillaume Briffoteaux

Bio: Guillaume Briffoteaux is an academic researcher from University of Mons. The author has contributed to research in topics: Surrogate model & Mathematical optimization. The author has an hindex of 3, co-authored 6 publications receiving 21 citations. Previous affiliations of Guillaume Briffoteaux include Lille University of Science and Technology & university of lille.

Parallel surrogate-assisted optimization: Batched Bayesian Neural Network-assisted GA versus q-EGO

Abstract: Surrogate-based optimization is widely used to deal with long-running black-box simulation-based objective functions. Actually, the use of a surrogate model such as Kriging or Artificial Neural Network allows to reduce the number of calls to the CPU time-intensive simulator. Bayesian optimization uses the ability of surrogates to provide useful information to help guiding effectively the optimization process. In this paper, the Efficient Global Optimization (EGO) reference framework is challenged by a Bayesian Neural Network-assisted Genetic Algorithm, namely BNN-GA. The Bayesian Neural Network (BNN) surrogate is chosen for its ability to provide an uncertainty measure of the prediction that allows to compute the Expected Improvement of a candidate solution in order to improve the exploration of the objective space. BNN is also more reliable than Kriging models for high-dimensional problems and faster to set up thanks to its incremental training. In addition, we propose a batch-based approach for the parallelization of BNN-GA that is challenged by a parallel version of EGO, called q-EGO. Parallel computing is a highly important complementary way (to surrogates) to deal with the computational burden of simulation-based optimization. The comparison of the two parallel approaches is experimentally performed through several benchmark functions and two real-world problems within the scope of Tuberculosis Transmission Control (TBTC). The study presented in this paper proves that parallel batched BNN-GA is a viable alternative to q-EGO approaches being more suitable for high-dimensional problems, parallelization impact, bigger data-bases and moderate search budgets. Moreover, a significant improvement of the solutions is obtained for the two TBTC problems tackled.

Optimising social mixing strategies to mitigate the impact of COVID-19 in six European countries: a mathematical modelling study

Abstract: Background If SARS-CoV-2 elimination is not feasible, strategies are needed to minimise the impact of COVID-19 in the medium-to-long term, until safe and effective vaccines can be used at the population-level. Methods Using a mathematical model, we identified contact mitigation strategies that minimised COVID-19-related deaths or years of life lost (YLLs) over a time-horizon of 15 months, using an intervention lasting six or 12 months, in Belgium, France, Italy, Spain, Sweden and the UK. We used strategies that either altered age- or location-specific contact patterns. The optimisation was performed under the constraint that herd immunity should be achieved by the end of the intervention period if post-infection immunity was persistent. We then tested the effect of waning immunity on the strategies. Findings Strategies of contact mitigation by age were much more effective than those based on mitigation by location. Extremely stringent contact reductions for individuals aged over 50 were required in most countries to minimise deaths or YLLs. The median final proportion of the population ever-infected with SARS-CoV-2 after herd immunity was reached ranged between 30% and 43%, depending on the country and intervention duration. Compared to an unmitigated scenario, optimised age-specific mitigation was predicted to avert over 1 million deaths across the six countries. The optimised scenarios assuming persistent immunity resulted in comparable hospital occupancies to that experienced during the March-April European wave. However, if immunity was shortlived, high burdens were expected without permanent contact mitigation. Interpretation Our analysis suggests that age-selective mitigation strategies can reduce the mortality impacts of COVID-19 dramatically even when significant transmission occurs. The stringency of the required restrictions in some groups raises concerns about the practicality of these strategies. If post-infection immunity was short-lived, solutions based on a mitigation period designed to increase population immunity should be accompanied with ongoing contact mitigation to prevent large epidemic resurgence.

Identification of the Parameter Values of the Constitutive and Friction Models in Machining Using EGO Algorithm: Application to Ti6Al4V

Abstract: The application of artificial intelligence and increasing high-speed computational performance is still not fully explored in the field of numerical modeling and simulation of machining processes. The efficiency of the numerical model to predict the observables depends on various inputs. The most important and challenging inputs are the material behavior of the work material and the friction conditions during the cutting operation. The parameters of the material model and the friction model have a decisive impact on the simulated results. To reduce the expensive experimentation cost that gives limited data for the parameters, an inverse methodology to identify the parameter values of those inputs is suggested to potentially have data of better quality. This paper introduces a novel approach for the inverse identification of model parameters by implementing the Efficient Global Optimization algorithm. In this work, a method relying on a complete automated Finite Element simulation-based optimization algorithm is implemented to inversely identify the value of the Johnson–Cook (JC) parameters and Coulomb’s friction coefficient correlatively, where the objective function is defined as minimizing the error difference between experimental and numerical results. The Ti6Al4V Grade 5 alloy material is considered as a work material, and the identified parameters sets are validated by comparing the simulated results with experimental results. The developed automation process reduces the computation time and eliminating human errors. The identified model parameters value predicts the cutting force as 169 N/mm (2% deviation from experiments), feed force as 55 N/mm (7% deviation from experiments), and chip thickness as 0.150 mm (11% deviation from experiments). Overall, the identified model parameters set improves the prediction accuracy of the finite element model by 32% compared with the best-identified parameters set in the literature.

Economic incentives for the development of new antibiotics : Report commissioned by the Public Health Agency of Sweden

Abstract: This report responds to a request by the Public Health Agency of Sweden (Folkhalsomyndigheten) concerning which incentives for antibiotics research and development (R&D) Sweden should take into ...

Parallel surrogate-assisted global optimization with expensive functions – a survey

Abstract: United States Dept of Energy (National Nuclear Security Administration Advanced Simulation and Computing Program Cooperative Agreement under the Predictive Academic Alliance Program DE-NA0002378)