A multi-fidelity active learning method for global design optimization problems with noisy evaluations
A multi-fidelity (MF) active learning method is presented for design optimization problems
characterized by noisy evaluations of the performance metrics. Namely, a generalized MF …
characterized by noisy evaluations of the performance metrics. Namely, a generalized MF …
Parametric model embedding
Methodologies for reducing the design-space dimensionality in shape optimization have
been recently developed based on unsupervised machine learning methods. These …
been recently developed based on unsupervised machine learning methods. These …
Analytical benchmark problems for multifidelity optimization methods
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 …
provide a set of stress tests for the assessment of multifidelity optimization methods. In …
Active learning and bayesian optimization: a unified perspective to learn with a goal
F Di Fiore, M Nardelli, L Mainini - Archives of Computational Methods in …, 2024 - Springer
Science and Engineering applications are typically associated with expensive optimization
problem to identify optimal design solutions and states of the system of interest. Bayesian …
problem to identify optimal design solutions and states of the system of interest. Bayesian …
Multi-fidelity gradient-based optimization for high-dimensional aeroelastic configurations
The simultaneous optimization of aircraft shape and internal structural size for transonic
flight is excessively costly. The analysis of the governing physics is expensive, in particular …
flight is excessively costly. The analysis of the governing physics is expensive, in particular …
Comparing multi-index stochastic collocation and multi-fidelity stochastic radial basis functions for forward uncertainty quantification of ship resistance
This paper presents a comparison of two multi-fidelity methods for the forward uncertainty
quantification of a naval engineering problem. Specifically, we consider the problem of …
quantification of a naval engineering problem. Specifically, we consider the problem of …
Design-space dimensionality reduction in global optimization of functional surfaces: recent developments and way forward
In shape optimization of complex industrial products (such as, but not limited to, hull forms,
rudder and appendages, propellers), there exists an inherent similarity between global …
rudder and appendages, propellers), there exists an inherent similarity between global …
[HTML][HTML] Physics-aware multifidelity Bayesian optimization: A generalized formulation
F Di Fiore, L Mainini - Computers & Structures, 2024 - Elsevier
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. Multifidelity …
by the significant computational cost required for their evaluation at every query. Multifidelity …
Efficient inverse design optimization through multi-fidelity simulations, machine learning, and boundary refinement strategies
This paper introduces a methodology designed to augment the inverse design optimization
process in scenarios constrained by limited compute, through the strategic synergy of multi …
process in scenarios constrained by limited compute, through the strategic synergy of multi …
Raal: Resource aware active learning for multifidelity efficient optimization
TRADITIONAL methods for black-box optimization require a considerable number of
evaluations of the objective function. This can be time consuming, impractical, and …
evaluations of the objective function. This can be time consuming, impractical, and …