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 2020-11-01 to 2024-11-01.)

Article	Citations
NSFnets (Navier-Stokes flow nets): Physics-informed neural networks for the incompressible Navier-Stokes equations	485
B-PINNs: Bayesian physics-informed neural networks for forward and inverse PDE problems with noisy data	468
When and why PINNs fail to train: A neural tangent kernel perspective	388
PhyGeoNet: Physics-informed geometry-adaptive convolutional neural networks for solving parameterized steady-state PDEs on irregular domain	262
Parallel physics-informed neural networks via domain decomposition	176
Constitutive artificial neural networks: A fast and general approach to predictive data-driven constitutive modeling by deep learning	134
A-PINN: Auxiliary physics informed neural networks for forward and inverse problems of nonlinear integro-differential equations	116
Physics-informed neural networks for inverse problems in supersonic flows	111
Transfer learning based multi-fidelity physics informed deep neural network	111
Direct shape optimization through deep reinforcement learning	111
DeepM&Mnet: Inferring the electroconvection multiphysics fields based on operator approximation by neural networks	110
A multi-resolution SPH method for fluid-structure interactions	108
Uncertainty quantification in scientific machine learning: Methods, metrics, and comparisons	108
Physics-informed machine learning for reduced-order modeling of nonlinear problems	108
Weak SINDy for partial differential equations	93
Physics-informed neural networks for solving forward and inverse flow problems via the Boltzmann-BGK formulation	89
Multi-fidelity Bayesian neural networks: Algorithms and applications	85
A fast and accurate physics-informed neural network reduced order model with shallow masked autoencoder	83
Self-adaptive physics-informed neural networks	80
Learning constitutive relations using symmetric positive definite neural networks	80
nPINNs: Nonlocal physics-informed neural networks for a parametrized nonlocal universal Laplacian operator. Algorithms and applications	80
Improving the accuracy and consistency of the scalar auxiliary variable (SAV) method with relaxation	78
A two-stage physics-informed neural network method based on conserved quantities and applications in localized wave solutions	76
DeepM&Mnet for hypersonics: Predicting the coupled flow and finite-rate chemistry behind a normal shock using neural-network approximation of operators	75
Adaptive multidimensional integration: vegas enhanced	72

DPM: A deep learning PDE augmentation method with application to large-eddy simulation	70
Thermodynamically consistent physics-informed neural networks for hyperbolic systems	67
Solving and learning nonlinear PDEs with Gaussian processes	65
Theory-guided hard constraint projection (HCP): A knowledge-based data-driven scientific machine learning method	63
The lattice Boltzmann method for nearly incompressible flows	60
Atomic cluster expansion: Completeness, efficiency and stability	59
A method for representing periodic functions and enforcing exactly periodic boundary conditions with deep neural networks	59
The mixed Deep Energy Method for resolving concentration features in finite strain hyperelasticity	57
Data-driven surrogate model with latent data assimilation: Application to wildfire forecasting	55
DeepMoD: Deep learning for model discovery in noisy data	53
Calibrate, emulate, sample	52
Hybrid FEM-NN models: Combining artificial neural networks with the finite element method	52
A cardiac electromechanical model coupled with a lumped-parameter model for closed-loop blood circulation	52
Unstructured un-split geometrical Volume-of-Fluid methods – A review	51
PFNN: A penalty-free neural network method for solving a class of second-order boundary-value problems on complex geometries	51
Deep learning of free boundary and Stefan problems	51
A stable SPH model with large CFL numbers for multi-phase flows with large density ratios	50
An immersed boundary fluid–structure interaction method for thin, highly compliant shell structures	50
A shock-stable modification of the HLLC Riemann solver with reduced numerical dissipation	49
Stable a posteriori LES of 2D turbulence using convolutional neural networks: Backscattering analysis and generalization to higher Re via transfer learning	48
A provably entropy stable subcell shock capturing approach for high order split form DG for the compressible Euler equations	48
Physics-informed PointNet: A deep learning solver for steady-state incompressible flows and thermal fields on multiple sets of irregular geometries	48
Gradient-based constrained optimization using a database of linear reduced-order models	48
Physics-informed neural networks for the shallow-water equations on the sphere	48