87 citations · 160 across the 5 of their papers we have counts for
6 papers
Physics-Aware Multifidelity Bayesian Optimization: a Generalized Formulation
Francesco Di Fiore, Laura Mainini
The adoption of high-fidelity models for many-query optimization problems is majorly limited by the significant computational cost required for their evaluation at every query. Mul…
Active Learning and Bayesian Optimization: a Unified Perspective to Learn with a Goal
Francesco Di Fiore, Michela Nardelli, Laura Mainini
Science and Engineering applications are typically associated with expensive optimization problems to identify optimal design solutions and states of the system of interest. Bayesi…
Non-Myopic Multifidelity Bayesian Optimization
Francesco Di Fiore, Laura Mainini
Bayesian optimization is a popular framework for the optimization of black box functions. Multifidelity methods allows to accelerate Bayesian optimization by exploiting low-fidelit…
Analytical Benchmark Problems for Multifidelity Optimization Methods
L. Mainini, A. Serani, M. P. Rumpfkeil +11
The paper presents a collection of analytical benchmark problems specifically selected to provide a set of stress tests for the assessment of multifidelity optimization methods. In…
Computational framework for real-time diagnostics and prognostics of aircraft actuation systems
Pier Carlo Berri, Matteo D. L. Dalla Vedova, Laura Mainini
Prognostics and Health Management (PHM) are emerging approaches to product life cycle that will maintain system safety and improve reliability, while reducing operating and mainten…
Resource Aware Multifidelity Active Learning for Efficient Optimization
Francesco Grassi, Giorgio Manganini, Michele Garraffa +1
Traditional methods for black box optimization require a considerable number of evaluations which can be time consuming, unpractical, and often unfeasible for many engineering appl…