Assessing non-nested configurations of multifidelity machine learning for quantum-chemical properties
Multifidelity machine learning (MFML) for quantum chemical properties has seen strong
development in the recent years. The method has been shown to reduce the cost of …
development in the recent years. The method has been shown to reduce the cost of …
[HTML][HTML] Cost-effective framework for gradual domain adaptation with multifidelity
S Sagawa, H Hino - Neural Networks, 2023 - Elsevier
In domain adaptation, when there is a large distance between the source and target
domains, the prediction performance will degrade. Gradual domain adaptation is one of the …
domains, the prediction performance will degrade. Gradual domain adaptation is one of the …
Multi-fidelity surrogate with heterogeneous input spaces for modeling melt pools in laser-directed energy deposition
N Menon, A Basak - arXiv preprint arXiv:2403.13136, 2024 - arxiv.org
Multi-fidelity (MF) modeling is a powerful statistical approach that can intelligently blend data
from varied fidelity sources. This approach finds a compelling application in predicting melt …
from varied fidelity sources. This approach finds a compelling application in predicting melt …