OOIR: Observatory of International Research

Papers

(The H4-Index of Journal of Computational Physics is 48. The table below lists those papers that are above that threshold based on CrossRef citation counts [max. 250 papers]. The publications cover those that have been published in the past four years, i.e., from 2020-04-01 to 2024-04-01.)

Article	Citations
NSFnets (Navier-Stokes flow nets): Physics-informed neural networks for the incompressible Navier-Stokes equations	379
B-PINNs: Bayesian physics-informed neural networks for forward and inverse PDE problems with noisy data	347
When and why PINNs fail to train: A neural tangent kernel perspective	265
PhyGeoNet: Physics-informed geometry-adaptive convolutional neural networks for solving parameterized steady-state PDEs on irregular domain	202
A deep-learning-based surrogate model for data assimilation in dynamic subsurface flow problems	149
RANS turbulence model development using CFD-driven machine learning	136
Weak adversarial networks for high-dimensional partial differential equations	132
Parallel physics-informed neural networks via domain decomposition	121
Data-driven POD-Galerkin reduced order model for turbulent flows	102
Constitutive artificial neural networks: A fast and general approach to predictive data-driven constitutive modeling by deep learning	100
DeepM&Mnet: Inferring the electroconvection multiphysics fields based on operator approximation by neural networks	90
Transfer learning based multi-fidelity physics informed deep neural network	90
A second-order and nonuniform time-stepping maximum-principle preserving scheme for time-fractional Allen-Cahn equations	86
Learning constitutive relations from indirect observations using deep neural networks	85
Direct shape optimization through deep reinforcement learning	85
Physics-informed machine learning for reduced-order modeling of nonlinear problems	83
Deep learning observables in computational fluid dynamics	83
A multi-resolution SPH method for fluid-structure interactions	82
Data-driven deep learning of partial differential equations in modal space	81
A parallel-in-time iterative algorithm for Volterra partial integro-differential problems with weakly singular kernel	77
Physics-informed neural networks for inverse problems in supersonic flows	75
A-PINN: Auxiliary physics informed neural networks for forward and inverse problems of nonlinear integro-differential equations	74
Enforcing statistical constraints in generative adversarial networks for modeling chaotic dynamical systems	73
Physics-informed neural networks for solving forward and inverse flow problems via the Boltzmann-BGK formulation	72
Recurrent neural network closure of parametric POD-Galerkin reduced-order models based on the Mori-Zwanzig formalism	68

Multi-fidelity Bayesian neural networks: Algorithms and applications	66
An immersed boundary-lattice Boltzmann method for fluid-structure interaction problems involving viscoelastic fluids and complex geometries	65
Weak SINDy for partial differential equations	65
On the stability of projection-based model order reduction for convection-dominated laminar and turbulent flows	65
Improving the accuracy and consistency of the scalar auxiliary variable (SAV) method with relaxation	65
Learning constitutive relations using symmetric positive definite neural networks	64
Uncertainty quantification in scientific machine learning: Methods, metrics, and comparisons	64
nPINNs: Nonlocal physics-informed neural networks for a parametrized nonlocal universal Laplacian operator. Algorithms and applications	63
DPM: A deep learning PDE augmentation method with application to large-eddy simulation	60
A fast and accurate physics-informed neural network reduced order model with shallow masked autoencoder	59
A volume-of-fluid method for interface-resolved simulations of phase-changing two-fluid flows	59
High order direct Arbitrary-Lagrangian-Eulerian schemes on moving Voronoi meshes with topology changes	59
Adaptive multidimensional integration: vegas enhanced	58
A two-stage physics-informed neural network method based on conserved quantities and applications in localized wave solutions	57
A computational model for nanosecond pulse laser-plasma interactions	57
DeepM&Mnet for hypersonics: Predicting the coupled flow and finite-rate chemistry behind a normal shock using neural-network approximation of operators	55
The lattice Boltzmann method for nearly incompressible flows	52
A generalized approximate control variate framework for multifidelity uncertainty quantification	52
Deep unsupervised learning of turbulence for inflow generation at various Reynolds numbers	50
Thermodynamically consistent physics-informed neural networks for hyperbolic systems	49
An efficient lattice Boltzmann method for compressible aerodynamics on D3Q19 lattice	49
Can we find steady-state solutions to multiscale rarefied gas flows within dozens of iterations?	49
DLGA-PDE: Discovery of PDEs with incomplete candidate library via combination of deep learning and genetic algorithm	49
Arbitrarily high-order linear energy stable schemes for gradient flow models	48