[HTML][HTML] Hydrothermal ageing of carbon fiber reinforced polymer composites applied for construction: A review
Carbon fiber reinforced polymer composites (CFRPs) have been widely used in civil
engineering due to light weight, high strength, corrosion resistance, etc. CFRPs can …
engineering due to light weight, high strength, corrosion resistance, etc. CFRPs can …
[HTML][HTML] Experimental characterization methods and numerical models of woven composite preforms: A review
T Yang, L Zhang, Z Li, K Huang, L Guo - Composites Part A: Applied …, 2024 - Elsevier
The formation of woven preforms is crucial for the quality and performance of textile
composites produced through the Liquid Composite Molding (LCM) process. However, the …
composites produced through the Liquid Composite Molding (LCM) process. However, the …
Multi-fidelity cost-aware Bayesian optimization
Bayesian optimization (BO) is increasingly employed in critical applications such as
materials design and drug discovery. An increasingly popular strategy in BO is to forgo the …
materials design and drug discovery. An increasingly popular strategy in BO is to forgo the …
Data fusion with latent map Gaussian processes
JT Eweis-Labolle, N Oune… - Journal of …, 2022 - asmedigitalcollection.asme.org
Multi-fidelity modeling and calibration are data fusion tasks that ubiquitously arise in
engineering design. However, there is currently a lack of general techniques that can jointly …
engineering design. However, there is currently a lack of general techniques that can jointly …
Globally approximate gaussian processes for big data with application to data-driven metamaterials design
We introduce a novel method for Gaussian process (GP) modeling of massive datasets
called globally approximate Gaussian process (GAGP). Unlike most large-scale supervised …
called globally approximate Gaussian process (GAGP). Unlike most large-scale supervised …
GP+: a python library for kernel-based learning via Gaussian Processes
In this paper we introduce GP+, an open-source library for kernel-based learning via
Gaussian processes (GPs) which are powerful statistical models that are completely …
Gaussian processes (GPs) which are powerful statistical models that are completely …
Probabilistic neural data fusion for learning from an arbitrary number of multi-fidelity data sets
C Mora, JT Eweis-Labolle, T Johnson, L Gadde… - Computer Methods in …, 2023 - Elsevier
In many applications in engineering and sciences analysts have simultaneous access to
multiple data sources. In such cases, the overall cost of acquiring information can be …
multiple data sources. In such cases, the overall cost of acquiring information can be …
[HTML][HTML] Multiscale modelling of material degradation and failure in plain woven composites: A novel approach for reliable predictions enabled by meta-models
In this paper a multiscale method for modelling damage evolution at meso and macro scales
with application to plain woven composites is presented for the first time. The method …
with application to plain woven composites is presented for the first time. The method …
Latent map Gaussian processes for mixed variable metamodeling
N Oune, R Bostanabad - Computer Methods in Applied Mechanics and …, 2021 - Elsevier
Gaussian processes (GPs) are ubiquitously used in sciences and engineering as
metamodels. Standard GPs, however, can only handle numerical or quantitative variables …
metamodels. Standard GPs, however, can only handle numerical or quantitative variables …
Peridynamic modeling of composite laminates with material coupling and transverse shear deformation
This study presents a new state-based PeriDynamic (PD) model of a composite laminate
with arbitrary laminate layup; it captures all types of material couplings in the presence of …
with arbitrary laminate layup; it captures all types of material couplings in the presence of …