A comprehensive and fair comparison of two neural operators (with practical extensions) based on FAIR data

Learning Functional Priors and Posteriors from Data and Physics

(2022) Xuhui Meng et al.   JOURNAL OF COMPUTATIONAL PHYSICS

A physics-informed variational DeepONet for predicting crack path in quasi-brittle materials

(2022) Somdatta Goswami et al.   COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING

Operator learning for predicting multiscale bubble growth dynamics

(2021) Chensen Lin et al.   JOURNAL OF CHEMICAL PHYSICS

A method for representing periodic functions and enforcing exactly periodic boundary conditions with deep neural networks

(2021) Suchuan Dong et al.   JOURNAL OF COMPUTATIONAL PHYSICS

DeepM&Mnet: Inferring the electroconvection multiphysics fields based on operator approximation by neural networks

(2021) Shengze Cai et al.   JOURNAL OF COMPUTATIONAL PHYSICS

Multi-fidelity Bayesian neural networks: Algorithms and applications

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

Bayesian neural networks for uncertainty quantification in data-driven materials modeling

(2021) Audrey Olivier et al.   COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING

DeepM&Mnet for hypersonics: Predicting the coupled flow and finite-rate chemistry behind a normal shock using neural-network approximation of operators

(2021) Zhiping Mao et al.   JOURNAL OF COMPUTATIONAL PHYSICS

A seamless multiscale operator neural network for inferring bubble dynamics

(2021) Chensen Lin et al.   JOURNAL OF FLUID MECHANICS

The unreasonable effectiveness of deep learning in artificial intelligence

(2020) Terrence J. Sejnowski   PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA

Extraction of mechanical properties of materials through deep learning from instrumented indentation

(2020) Lu Lu et al.   PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA

A physics-informed operator regression framework for extracting data-driven continuum models

(2020) Ravi G. Patel et al.   COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING

B-PINNs: Bayesian physics-informed neural networks for forward and inverse PDE problems with noisy data

(2020) Liu Yang et al.   JOURNAL OF COMPUTATIONAL PHYSICS

Systems biology informed deep learning for inferring parameters and hidden dynamics

(2020) Alireza Yazdani et al.   PLoS Computational Biology

Data-driven learning of nonlocal physics from high-fidelity synthetic data

(2020) Huaiqian You et al.   COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING

Quantifying total uncertainty in physics-informed neural networks for solving forward and inverse stochastic problems

(2019) Dongkun Zhang et al.   JOURNAL OF COMPUTATIONAL PHYSICS

A composite neural network that learns from multi-fidelity data: Application to function approximation and inverse PDE problems

(2019) Xuhui Meng et al.   JOURNAL OF COMPUTATIONAL PHYSICS

The FAIR Guiding Principles for scientific data management and stewardship

(2016) Mark D. Wilkinson et al.   Scientific Data

Multiple-relaxation-time lattice Boltzmann model for incompressible miscible flow with large viscosity ratio and high Péclet number

(2015) Xuhui Meng et al.   PHYSICAL REVIEW E

Positivity-preserving high order discontinuous Galerkin schemes for compressible Euler equations with source terms

(2010) Xiangxiong Zhang et al.   JOURNAL OF COMPUTATIONAL PHYSICS

High-order well-balanced schemes and applications to non-equilibrium flow

(2009) Wei Wang et al.   JOURNAL OF COMPUTATIONAL PHYSICS