OOIR: Observatory of International Research

Papers

(The H4-Index of Computer Methods in Applied Mechanics and Engineering is 63. 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
The Arithmetic Optimization Algorithm	1788
Dwarf Mongoose Optimization Algorithm	555
Artificial hummingbird algorithm: A new bio-inspired optimizer with its engineering applications	512
A physics-informed deep learning framework for inversion and surrogate modeling in solid mechanics	464
hp-VPINNs: Variational physics-informed neural networks with domain decomposition	302
SciANN: A Keras/TensorFlow wrapper for scientific computations and physics-informed deep learning using artificial neural networks	221
On the eigenvector bias of Fourier feature networks: From regression to solving multi-scale PDEs with physics-informed neural networks	221
Gradient-enhanced physics-informed neural networks for forward and inverse PDE problems	220
Starling murmuration optimizer: A novel bio-inspired algorithm for global and engineering optimization	184
Multi-fidelity deep neural network surrogate model for aerodynamic shape optimization	174
A comprehensive and fair comparison of two neural operators (with practical extensions) based on FAIR data	166
A physics-informed variational DeepONet for predicting crack path in quasi-brittle materials	156
A comprehensive study of non-adaptive and residual-based adaptive sampling for physics-informed neural networks	151
Deep generative modeling for mechanistic-based learning and design of metamaterial systems	150
Geometric deep learning for computational mechanics Part I: anisotropic hyperelasticity	134
POD-DL-ROM: Enhancing deep learning-based reduced order models for nonlinear parametrized PDEs by proper orthogonal decomposition	124
Parametric deep energy approach for elasticity accounting for strain gradient effects	122
Smart constitutive laws: Inelastic homogenization through machine learning	113
Exact imposition of boundary conditions with distance functions in physics-informed deep neural networks	111
Unsupervised discovery of interpretable hyperelastic constitutive laws	108
Sobolev training of thermodynamic-informed neural networks for interpretable elasto-plasticity models with level set hardening	107
Modified couple stress-based geometrically nonlinear oscillations of porous functionally graded microplates using NURBS-based isogeometric approach	106
Multi-level convolutional autoencoder networks for parametric prediction of spatio-temporal dynamics	105
Hybrid enhanced Monte Carlo simulation coupled with advanced machine learning approach for accurate and efficient structural reliability analysis	105
Physics-informed graph neural Galerkin networks: A unified framework for solving PDE-governed forward and inverse problems	104

Non-invasive inference of thrombus material properties with physics-informed neural networks	99
Physics informed neural networks for continuum micromechanics	98
Fracture of thermo-elastic solids: Phase-field modeling and new results with an efficient monolithic solver	94
CAN-PINN: A fast physics-informed neural network based on coupled-automatic–numerical differentiation method	94
Deep-learning-based surrogate flow modeling and geological parameterization for data assimilation in 3D subsurface flow	94
Hierarchical Deep Learning Neural Network (HiDeNN): An artificial intelligence (AI) framework for computational science and engineering	93
A novel sequential method to train physics informed neural networks for Allen Cahn and Cahn Hilliard equations	92
An enhanced hybrid arithmetic optimization algorithm for engineering applications	90
Data-driven topology optimization of spinodoid metamaterials with seamlessly tunable anisotropy	88
Physics-informed neural network for modelling the thermochemical curing process of composite-tool systems during manufacture	87
MOMPA: Multi-objective marine predator algorithm	86
A physics-guided neural network framework for elastic plates: Comparison of governing equations-based and energy-based approaches	86
A new family of Constitutive Artificial Neural Networks towards automated model discovery	86
PhyCRNet: Physics-informed convolutional-recurrent network for solving spatiotemporal PDEs	85
Data-driven fracture mechanics	81
Phase field modelling of fracture and fatigue in Shape Memory Alloys	79
DiscretizationNet: A machine-learning based solver for Navier–Stokes equations using finite volume discretization	78
An effective multi-objective artificial hummingbird algorithm with dynamic elimination-based crowding distance for solving engineering design problems	74
Three-dimensional phase-field modeling of mode I + II/III failure in solids	74
Double-phase-field formulation for mixed-mode fracture in rocks	74
Design of graded lattice sandwich structures by multiscale topology optimization	74
Local extreme learning machines and domain decomposition for solving linear and nonlinear partial differential equations	72
Universal machine learning for topology optimization	72
Young’s double-slit experiment optimizer : A novel metaheuristic optimization algorithm for global and constraint optimization problems	70
A mixed formulation for physics-informed neural networks as a potential solver for engineering problems in heterogeneous domains: Comparison with finite element method	70
A generalised phase field model for fatigue crack growth in elastic–plastic solids with an efficient monolithic solver	70
Additive manufacturing oriented topology optimization of structures with self-supported enclosed voids	69
A nonlocal physics-informed deep learning framework using the peridynamic differential operator	68
Body-fitted topology optimization of 2D and 3D fluid-to-fluid heat exchangers	68
Stress constrained multi-material topology optimization with the ordered SIMP method	68
A generic physics-informed neural network-based constitutive model for soft biological tissues	67
Operator inference for non-intrusive model reduction of systems with non-polynomial nonlinear terms	66
Modeling cardiac muscle fibers in ventricular and atrial electrophysiology simulations	66
A meshfree stabilized collocation method (SCM) based on reproducing kernel approximation	65
Bayesian neural networks for uncertainty quantification in data-driven materials modeling	65
A novel fully-decoupled, second-order and energy stable numerical scheme of the conserved Allen–Cahn type flow-coupled binary surfactant model	65
Hybrid and enhanced PSO: Novel first order reliability method-based hybrid intelligent approaches	64
Novel hybrid robust method for uncertain reliability analysis using finite conjugate map	63
PINN-FORM: A new physics-informed neural network for reliability analysis with partial differential equation	63