FastSVD-ML–ROM: A reduced-order modeling framework based on machine learning for real-time applications
GI Drakoulas, TV Gortsas, GC Bourantas… - Computer Methods in …, 2023 - Elsevier
Digital twins have emerged as a key technology for optimizing the performance of
engineering products and systems. High-fidelity numerical simulations constitute the …
engineering products and systems. High-fidelity numerical simulations constitute the …
Ensemble Kalman filtering meets Gaussian process SSM for non-mean-field and online inference
The Gaussian process state-space models (GPSSMs) represent a versatile class of data-
driven nonlinear dynamical system models. However, the presence of numerous latent …
driven nonlinear dynamical system models. However, the presence of numerous latent …
An explainable machine learning-based probabilistic framework for the design of scaffolds in bone tissue engineering
Recently, 3D-printed biodegradable scaffolds have shown great potential for bone repair in
critical-size fractures. The differentiation of the cells on a scaffold is impacted among other …
critical-size fractures. The differentiation of the cells on a scaffold is impacted among other …
Bayesian optimization with ensemble learning models and adaptive expected improvement
Optimizing a black-box function that is expensive to evaluate emerges in a gamut of
machine learning and artificial intelligence applications including drug discovery, policy …
machine learning and artificial intelligence applications including drug discovery, policy …
Physics-based reduced order modeling for uncertainty quantification of guided wave propagation using bayesian optimization
Guided wave propagation (GWP) is commonly employed for the design of SHM systems.
However, GWP is sensitive to variations in the material properties, often leading to false …
However, GWP is sensitive to variations in the material properties, often leading to false …
Weighted ensembles for active learning with adaptivity
Labeled data can be expensive to acquire in several application domains, including medical
imaging, robotics, and computer vision. To efficiently train machine learning models under …
imaging, robotics, and computer vision. To efficiently train machine learning models under …
Surrogate modeling for Bayesian optimization beyond a single Gaussian process
Bayesian optimization (BO) has well-documented merits for optimizing black-box functions
with an expensive evaluation cost. Such functions emerge in applications as diverse as …
with an expensive evaluation cost. Such functions emerge in applications as diverse as …
Graph-guided gaussian process-based diagnosis of cvd severity with uncertainty measures
The severity of coronary artery disease can be assessed invasively using the Fractional
Flow Reserve (FFR) index which is a useful diagnostic tool for the clinicians to select the …
Flow Reserve (FFR) index which is a useful diagnostic tool for the clinicians to select the …
Active sampling over graphs for Bayesian reconstruction with Gaussian ensembles
Graph-guided semi-supervised learning (SSL) has gained popularity in several network
science applications, including biological, social, and financial ones. SSL becomes …
science applications, including biological, social, and financial ones. SSL becomes …
Bayesian optimization for online management in dynamic mobile edge computing
Recent years have witnessed the emergence of mobile edge computing (MEC), on the
premise of a cost-effective enhancement in the computational ability of hardware …
premise of a cost-effective enhancement in the computational ability of hardware …