130 citations · 327 across the 10 of their papers we have counts for
10 papers
PDE-constrained Gaussian process surrogate modeling with uncertain data locations
Dongwei Ye, Weihao Yan, Christoph Brune +1
Gaussian process regression is widely applied in computational science and engineering for surrogate modeling owning to its kernel-based and probabilistic nature. In this work, we…
Uncertainty quantification for nonlinear solid mechanics using reduced order models with Gaussian process regression
Ludovica Cicci, Stefania Fresca, Mengwu Guo +2
Uncertainty quantification (UQ) tasks, such as sensitivity analysis and parameter estimation, entail a huge computational complexity when dealing with input-output maps involving t…
Deep Kernel Learning of Dynamical Models from High-Dimensional Noisy Data
Nicolò Botteghi, Mengwu Guo, Christoph Brune
This work proposes a Stochastic Variational Deep Kernel Learning method for the data-driven discovery of low-dimensional dynamical models from high-dimensional noisy data. The fram…
Multi-fidelity surrogate modeling using long short-term memory networks
Paolo Conti, Mengwu Guo, Andrea Manzoni +1
When evaluating quantities of interest that depend on the solutions to differential equations, we inevitably face the trade-off between accuracy and efficiency. Especially for para…
Bayesian operator inference for data-driven reduced-order modeling
Mengwu Guo, Shane A. McQuarrie, Karen E. Willcox
This work proposes a Bayesian inference method for the reduced-order modeling of time-dependent systems. Informed by the structure of the governing equations, the task of learning…
A brief note on understanding neural networks as Gaussian processes
Mengwu Guo
As a generalization of the work in [Lee et al., 2017], this note briefly discusses when the prior of a neural network output follows a Gaussian process, and how a neural-network-in…
Multi-fidelity regression using artificial neural networks: efficient approximation of parameter-dependent output quantities
Mengwu Guo, Andrea Manzoni, Maurice Amendt +2
Highly accurate numerical or physical experiments are often time-consuming or expensive to obtain. When time or budget restrictions prohibit the generation of additional data, the…
Energy-based error bound of physics-informed neural network solutions in elasticity
Mengwu Guo, Ehsan Haghighat
An energy-based a posteriori error bound is proposed for the physics-informed neural network solutions of elasticity problems. An admissible displacement-stress solution pair is ob…
Legendre-Fenchel duality and a generalized constitutive relation error
Mengwu Guo, Weimin Han, Hongzhi Zhong
A generalized constitutive relation error is proposed in an analogous form to Fenchel-Young inequality on the basis of the key idea of Legendre-Fenchel duality theory. The generali…
Strict upper and lower bounds for quantities of interest in static response sensitivity analysis
Mengwu Guo, Hongzhi Zhong
In this paper, a goal-oriented error estimation technique for static response sensitivity analysis is proposed based on the constitutive relation error (CRE) estimation for finite…