Multi-fidelity regression using artificial neural networks: Efficient approximation of parameter-dependent output quantities

Multi-fidelity Bayesian neural networks: Algorithms and applications

(2021) Xuhui Meng et al.   JOURNAL OF COMPUTATIONAL PHYSICS

Generalizing from a Few Examples

(2020) Yaqing Wang et al.   ACM COMPUTING SURVEYS

A non-intrusive multifidelity method for the reduced order modeling of nonlinear problems

(2020) Mariella Kast et al.   COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING

Machine learning of multiscale active force generation models for the efficient simulation of cardiac electromechanics

(2020) F. Regazzoni et al.   COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING

General Multi-Fidelity Framework for Training Artificial Neural Networks With Computational Models

(2019) Roland Can Aydin et al.   Frontiers in Materials

Multi-Fidelity Physics-Constrained Neural Network and Its Application in Materials Modeling

(2019) Dehao Liu et al.   JOURNAL OF MECHANICAL DESIGN

Model reduction of dynamical systems on nonlinear manifolds using deep convolutional autoencoders

(2019) Kookjin Lee et al.   JOURNAL OF COMPUTATIONAL PHYSICS

An artificial neural network as a troubled-cell indicator

(2018) Deep Ray et al.   JOURNAL OF COMPUTATIONAL PHYSICS

Non-intrusive reduced order modeling of nonlinear problems using neural networks

(2018) J.S. Hesthaven et al.   JOURNAL OF COMPUTATIONAL PHYSICS

DGM: A deep learning algorithm for solving partial differential equations

(2018) Justin Sirignano et al.   JOURNAL OF COMPUTATIONAL PHYSICS

Survey of Multifidelity Methods in Uncertainty Propagation, Inference, and Optimization

(2018) Benjamin Peherstorfer et al.   SIAM REVIEW

Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations

(2018) M. Raissi et al.   JOURNAL OF COMPUTATIONAL PHYSICS

Inferring solutions of differential equations using noisy multi-fidelity data

(2017) Maziar Raissi et al.   JOURNAL OF COMPUTATIONAL PHYSICS

A brief introduction to weakly supervised learning

(2017) Zhi-Hua Zhou   National Science Review

Efficient model reduction of parametrized systems by matrix discrete empirical interpolation

(2015) Federico Negri et al.   JOURNAL OF COMPUTATIONAL PHYSICS

Preserving Lagrangian Structure in Nonlinear Model Reduction with Application to Structural Dynamics

(2015) Kevin Carlberg et al.   SIAM JOURNAL ON SCIENTIFIC COMPUTING

A Survey of Projection-Based Model Reduction Methods for Parametric Dynamical Systems

(2015) Peter Benner et al.   SIAM REVIEW

Nonlinear Model Reduction via Discrete Empirical Interpolation

(2010) Saifon Chaturantabut et al.   SIAM JOURNAL ON SCIENTIFIC COMPUTING