Combining additivity and active subspaces for high-dimensional Gaussian process modeling
The paper addresses high-dimensional Gaussian process modeling challenges by proposing a hybrid multi-fidelity model combining first-order additive models and linear embeddings, enhanced by active subspace learning. This approach combines the trend-capturing nature of additivity with the interaction learning power of active subspaces to improve performance over standard methods.