Bayesian optimization with active learning of design constraints using an entropy-based approach
The design of alloys for use in gas turbine engine blades is a complex task that involves
balancing multiple objectives and constraints. Candidate alloys must be ductile at room …
balancing multiple objectives and constraints. Candidate alloys must be ductile at room …
A perspective on Bayesian methods applied to materials discovery and design
For more than two decades, there has been increasing interest in developing frameworks for
the accelerated discovery and design of novel materials that could enable promising and …
the accelerated discovery and design of novel materials that could enable promising and …
Multi-objective materials bayesian optimization with active learning of design constraints: Design of ductile refractory multi-principal-element alloys
Bayesian Optimization (BO) has emerged as a powerful framework to efficiently explore and
exploit materials design spaces. To date, most BO approaches to materials design have …
exploit materials design spaces. To date, most BO approaches to materials design have …
Multi‐fidelity data fusion through parameter space reduction with applications to automotive engineering
Multi‐fidelity models are of great importance due to their capability of fusing information
coming from different numerical simulations, surrogates, and sensors. We focus on the …
coming from different numerical simulations, surrogates, and sensors. We focus on the …
Bayesian optimization objective-based experimental design
M Imani, SF Ghoreishi - 2020 American control conference …, 2020 - ieeexplore.ieee.org
Design has become a salient part of most of the scientific and engineering tasks, embracing
a wide range of domains including real experimental settings (eg, material discovery or drug …
a wide range of domains including real experimental settings (eg, material discovery or drug …
[图书][B] Advanced Reduced Order Methods and Applications in Computational Fluid Dynamics
Reduced order modeling is an important and fast-growing research field in computational
science and engineering, motivated by several reasons, of which we mention just a few …
science and engineering, motivated by several reasons, of which we mention just a few …
Adaptive active subspace-based efficient multifidelity materials design
D Khatamsaz, A Molkeri, R Couperthwaite, J James… - Materials & Design, 2021 - Elsevier
Materials design calls for an optimal exploration and exploitation of the process-structure-
property (PSP) relationships to produce materials with targeted properties. Recently, we …
property (PSP) relationships to produce materials with targeted properties. Recently, we …
Boolean Kalman filter and smoother under model uncertainty
Partially-observed Boolean dynamical systems (POBDS) are a general class of nonlinear
state-space models that provide a rich framework for modeling many complex dynamical …
state-space models that provide a rich framework for modeling many complex dynamical …
Efficiently exploiting process-structure-property relationships in material design by multi-information source fusion
D Khatamsaz, A Molkeri, R Couperthwaite, J James… - Acta Materialia, 2021 - Elsevier
Materials design calls for the (inverse) exploitation of Process-Structure-Property (PSP)
relationships to produce materials with targeted properties. Unfortunately, most materials …
relationships to produce materials with targeted properties. Unfortunately, most materials …
On the importance of microstructure information in materials design: PSP vs PP
A Molkeri, D Khatamsaz, R Couperthwaite, J James… - Acta Materialia, 2022 - Elsevier
The focus of goal-oriented materials design is to find the necessary chemistry/processing
conditions to achieve the desired properties. In this setting, a material's microstructure is …
conditions to achieve the desired properties. In this setting, a material's microstructure is …