Bayesian emulation of complex multi-output and dynamic computer models

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Kernels for Vector-Valued Functions: a Review (2011, methodology): “Examples include [42] and [23] under the LMC framework or [14] and [99] under the process convolution approach.”
A framework for data-driven analysis of materials under uncertainty: Countering the curse of dimensionality (2017, methodology): “Alternatively, a multiresponse Gaussian Process model may be used [119].”
Globally Approximate Gaussian Processes for Big Data With Application to Data-Driven Metamaterials Design (2019, unknown): “[45 – 48] for details).”
Efficient Emulators for Multivariate Deterministic Functions (2008, background): “emulators for functions with vector outputs (Craig et al., 1997, 2001; Kennedy and O’Hagan, 2001; Bayarri et al., 2005; Conti and O’Hagan, 2007; Higdon et al., 2007; Bayarri et al., 2007).”
Multi-output separable Gaussian process: Towards an efficient, fully Bayesian paradigm for uncertainty quantification (2013, methodology): “[16,17] model non-stationary responses, while [18,15] (in quite different ways) attempt to capture correlations between multiple outputs.”
Predicting Vehicle Crashworthiness: Validation of Computer Models for Functional and Hierarchical Data (2009, methodology): “Similar approaches are taken in Conti and O’Hagan (2007), Rougier (2007), and McFarkand et al. (2008).”