Strict upper and lower bounds for quantities of interest in static response sensitivity analysis. - Mengwu Guo; Hongzhi Zhong (CoRR, 2016)

Weak form quadrature solution of 2mth-order Fredholm integro-differential equations. - Mengwu Guo; Hongzhi Zhong (Int. J. Comput. Math., 2016)

Constitutive-relation-error-based a posteriori error bounds for a class of elliptic variational inequalities. - Mengwu Guo; Hongzhi Zhong (Appl. Math. Lett., 2017)

An energy-based error bound of physics-informed neural network solutions in elasticity. - Mengwu Guo; Ehsan Haghighat (CoRR, 2020)

Multi-fidelity regression using artificial neural networks: efficient approximation of parameter-dependent output quantities. - Mengwu Guo; Andrea Manzoni; Maurice Amendt; Paolo Conti; Jan S. Hesthaven (CoRR, 2021)

A brief note on understanding neural networks as Gaussian processes. - Mengwu Guo (CoRR, 2021)

Bayesian operator inference for data-driven reduced-order modeling. - Mengwu Guo; Shane A. McQuarrie; Karen E. Willcox (CoRR, 2022)

Multi-fidelity surrogate modeling using long short-term memory networks. - Paolo Conti; Mengwu Guo; Andrea Manzoni; Jan S. Hesthaven (CoRR, 2022)

Deep Kernel Learning of Dynamical Models from High-Dimensional Noisy Data. - Nicolò Botteghi; Mengwu Guo; Christoph Brune (CoRR, 2022)

Bayesian approach to Gaussian process regression with uncertain inputs. - Dongwei Ye; Mengwu Guo (CoRR, 2023)

Multi-fidelity reduced-order surrogate modeling. - Paolo Conti; Mengwu Guo; Andrea Manzoni; Attilio Frangi; Steven L. Brunton; J. Nathan Kutz (CoRR, 2023)

Uncertainty quantification for nonlinear solid mechanics using reduced order models with Gaussian process regression. - Ludovica Cicci; Stefania Fresca; Mengwu Guo; Andrea Manzoni; Paolo Zunino (Comput. Math. Appl., 2023)

Gaussian process learning of nonlinear dynamics. - Dongwei Ye; Mengwu Guo (Commun. Nonlinear Sci. Numer. Simul., 2024)

Recurrent Deep Kernel Learning of Dynamical Systems. - Nicolò Botteghi; Paolo Motta; Andrea Manzoni; Paolo Zunino; Mengwu Guo (CoRR, 2024)

Sparsifying dimensionality reduction of PDE solution data with Bregman learning. - Tjeerd Jan Heeringa; Christoph Brune; Mengwu Guo (CoRR, 2024)

Bayesian learning with Gaussian processes for low-dimensional representations of time-dependent nonlinear systems. - Shane A. McQuarrie; Anirban Chaudhuri; Karen E. Willcox; Mengwu Guo (CoRR, 2024)

HypeRL: Parameter-Informed Reinforcement Learning for Parametric PDEs. - Nicolò Botteghi; Stefania Fresca; Mengwu Guo; Andrea Manzoni (CoRR, 2025)

PDE-DKL: PDE-constrained deep kernel learning in high dimensionality. - Weihao Yan; Christoph Brune; Mengwu Guo (CoRR, 2025)

Towards scientific machine learning for granular material simulations - challenges and opportunities. - Marc Fransen; Andreas Fürst; Deepak Tunuguntla; Daniel N. Wilke; Benedikt Alkin; Daniel Barreto; Johannes Brandstetter; Miguel Angel Cabrera; Xinyan Fan; Mengwu Guo; Bram Kieskamp; Krishna Kumar; et al. (CoRR, 2025)