Nonlinear dimensionality reduction and Bayesian optimization for accelerating design of materials

Abstract Optimizing biobased foam formulations is challenging because experiments are costly and fast-to-measure surrogate properties occupy high-dimensional spaces. Bayesian optimization (BO) with Gaussian process regression (GPR) can guide data-efficient searches, but its performance depends on th...

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Principais autores:	Muhammad Osman Nadeem Farooqui, Isaac Y. Miranda-Valdez, Tero Mäkinen, Juha Koivisto, Mikko J. Alava
Format:	Artigo
Jezik:	Inglês
Izdano:	Nature Portfolio 2026-05-01
Serija:	Scientific Reports
Teme:	Dimensionality reduction (DR) Principal component analysis (PCA) T-distributed stochastic neighbor embedding (t-SNE) Uniform manifold approximation and projection (UMAP) Gaussian process (GP) Bayesian optimization (BO)
Online dostop:	https://doi.org/10.1038/s41598-026-51517-8
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