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 …

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 …

Resided at the intersection of multi-fidelity optimization (MFO) and Bayesian optimization
(BO), MF BO has found a niche in solving expensive engineering design optimization …

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 …

Recently, there has been a growing interest for mixed-categorical meta-models based on
Gaussian process (GP) surrogates. In this setting, several existing approaches use different …

Uncertainty quantification is a critical aspect of machine learning models for material
property predictions. Gaussian Process Regression (GPR) is a popular technique for …

Topology optimization (TO) provides a principled mathematical approach for optimizing the
performance of a structure by designing its material spatial distribution in a pre-defined …

In this work, a data-driven framework based on Phase-Field simulations data is proposed to
highlight the capabilities of neural networks to ensure accurate low dimensionality reduction …

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 …

Computational modeling of heterogeneous materials is increasingly relying on multiscale
simulations which typically leverage the homogenization theory for scale coupling. Such …