Key papers

• Constructing Extreme Heatwave Storylines with Differentiable Climate Models (2025)
  Tim Whittaker and Alejandro Di Luca
  5.40
• Pore-scale distribution and transport of active particles in a two-dimensional lattice (2026)
  Akhil Varma et al.
  5.01
• Uncertainty quantification and stability of neural operators for prediction of three-dimensional turbulence (2025)
  Xintong Zou et al.
  4.98
• Representing Flow Fields with Divergence-Free Kernels for Reconstruction (2025)
  Xingyu Ni et al.
  4.87
• Spatiotemporal wall pressure forecast of a rectangular cylinder with physics-aware DeepU-Fourier neural network (2025)
  Junle Liu et al.
  4.58
• Gradient-free learning of a closed-loop wall controller for turbulent drag reduction (2026)
  Giorgio Maria Cavallazzi et al.
  4.39
• Spatio-Temporal Prediction of Unsteady Airfoil Aerodynamics Using Augmented Graph Neural Ordinary Differential Equations with Exogenous Controls (2026)
  Henrik Lange et al.
  4.39
• Geometry-Aware Anisotropic Boundary Correction for Aerodynamic Simulation (2026)
  Xin Zhang et al.
  4.33
• High-fidelity Modeling of Full-scale Pressurized Water Reactor Flow Fields for Machine Learning Applications (2026)
  Logan A. Burnett et al.
  4.27
• Accelerating Bayesian inverse design in computational fluid dynamics using neural operators (2026)
  Bipin Tiwari et al.
  4.27
• Neural Operator-Based Surrogate Model for CFD:Helical Coil Steam Generator in Small Modular Reactor (2026)
  Minseo Lee et al.
  4.27
• Update hydrological states or meteorological forcings? Comparing data assimilation methods for differentiable hydrologic models (2025)
  Amirmoez Jamaat et al.
  3.59
• Self-Consistent Flow: Unifying Velocity and Endpoint Prediction for Rectified Flow Models (2026)
  Xu Han et al.
  3.51
• Ensemble Controlled-Flow Filtering for Implicit Data Assimilation (2026)
  Zhuoyuan Li et al.
  3.51
• An adaptive, data-driven multiscale approach for dense granular flows (2025)
  B. Siddani et al.
  2.82
• Geometry Matters: Benchmarking Scientific ML Approaches for Flow Prediction around Complex Geometries (2025)
  Ali Rabeh et al.
  2.60
• Spectral-Refiner: Accurate Fine-Tuning of Spatiotemporal Fourier Neural Operator for Turbulent Flows (2024)
  Shuhao Cao et al.
  2.21
• Dynamic Deep Learning Based Super-Resolution For The Shallow Water Equations (2024)
  Maximilian Witte et al.
  2.15
• HiLiftAeroML: High-Fidelity Computational Fluid Dynamics Dataset for High-Lift Aircraft Aerodynamics (2026)
  Neil Ashton et al.
  1.89
• LSTM-PINN for Steady-State Electrothermal Transport: Preserving Multi-Field Consis tency in Strongly Coupled Heat and Fluid Flow (2026)
  Yuqing Zhou et al.
  1.83
• Glassy phase transition in immiscible steady-state two-phase flow in porous media (2026)
  Santanu Sinha et al.
  1.78
• Multifidelity Surrogate Modeling of Depressurized Loss of Forced Cooling in High-temperature Gas Reactors (2026)
  Meredith Eaheart et al.
  1.78
• Anisotropic Permeability Tensor Prediction from Porous Media Microstructure via Physics-Informed Progressive Transfer Learning with Hybrid CNN-Transformer (2026)
  Mohammad Nooraiepour
  1.78
• SLE-FNO: Single-Layer Extensions for Task-Agnostic Continual Learning in Fourier Neural Operators (2026)
  Mahmoud Elhadidy and Roshan M. D'Souza and Amirhossein Arzani
  1.78
• BOOST-RPF: Boosted Sequential Trees for Radial Power Flow (2026)
  Ehimare Okoyomon et al.
  1.78
• RMFlow: Refined Mean Flow by a Noise-Injection Step for Multimodal Generation (2026)
  Yuhao Huang and Shih-Hsin Wang and Andrea L. Bertozzi and Bao Wang
  1.72
• Learning Flow Distributions via Projection-Constrained Diffusion on Manifolds (2026)
  Noah Trupin et al.
  1.72
• Refining Graphical Neural Network Predictions Using Flow Matching for Optimal Power Flow with Constraint-Satisfaction Guarantee (2025)
  Kshitiz Khanal
  1.61
• Uncertainty-Aware Flow Field Reconstruction Using SVGP Kolmogorov-Arnold Networks (2025)
  Y. Sungtaek Ju
  1.61
• RheOFormer: A generative transformer model for simulation of complex fluids and flows (2025)
  Maedeh Saberi et al.
  1.50
• Residual Correction Models for AC Optimal Power Flow Using DC Optimal Power Flow Solutions (2025)
  Muhy Eddin Za'ter et al.
  1.50
• A reduced-order derivative-informed neural operator for subsurface fluid-flow (2025)
  Jeongjin Park et al.
  1.44
• Graph-Based Learning of Free Surface Dynamics in Generalized Newtonian Fluids using Smoothed Particle Hydrodynamics (2025)
  Hyo-Jin Kim et al.
  1.44
• Hierarchical Dimensionless Learning (Hi-{\pi}): A physics-data hybrid-driven approach for discovering dimensionless parameter combinations (2025)
  Mingkun Xia et al.
  1.33
• Learning Non-Ideal Vortex Flows Using the Differentiable Vortex Particle Method (2025)
  Ziqi Ji et al.
  1.11
• Scalable Discovery of Fundamental Physical Laws: Learning Magnetohydrodynamics from 3D Turbulence Data (2025)
  Matthew Golden et al.
  1.00
• CNN-powered micro- to macro-scale flow modeling in deformable porous media (2025)
  Yousef Heider et al.
  1.00
• Prediction of Multiscale Features Using Deep Learning-based Preconditioner-Solver Architecture for Darcy Equation in High-Contrast Media (2024)
  Jie Chen et al.
  0.94
• Multifidelity Computing for Coupling Full and Reduced Order Models (2020)
  Shady E. Ahmed et al.
  —
• Frequency-compensated PINNs for Fluid-dynamic Design Problems (2020)
  Tongtao Zhang et al.
  —
• Learned Turbulence Modelling with Differentiable Fluid Solvers: Physics-based Loss-functions and Optimisation Horizons (2022)
  Bj\"orn List et al.
  —
• Leveraging Stochastic Predictions of Bayesian Neural Networks for Fluid Simulations (2022)
  Maximilian Mueller et al.
  —
• Machine learning based surrogate models for microchannel heat sink optimization (2022)
  Ante Sikirica et al.
  —
• Combined mechanistic and machine learning method for construction of oil reservoir permeability map consistent with well test measurements (2023)
  E. A. Kanin et al.
  —
• Turbulence Model Development based on a Novel Method Combining Gene Expression Programming with an Artificial Neural Network (2023)
  Haochen Li et al.
  —
• Multiscale Graph Neural Network Autoencoders for Interpretable Scientific Machine Learning (2023)
  Shivam Barwey et al.
  —
• Neural Astrophysical Wind Models (2023)
  Dustin D. Nguyen
  —
• Turbulence in Focus: Benchmarking Scaling Behavior of 3D Volumetric Super-Resolution with BLASTNet 2.0 Data (2023)
  Wai Tong Chung et al.
  —
• Structure-Informed Neural Networks for Boundary Observation Problems (2023)
  Jakub Horsky and Andrew Wynn
  —
• A Physics-Guided Bi-Fidelity Fourier-Featured Operator Learning Framework for Predicting Time Evolution of Drag and Lift Coefficients (2023)
  Amirhossein Mollaali et al.
  —
• RONAALP: Reduced-Order Nonlinear Approximation with Active Learning Procedure (2023)
  Cl\'ement Scherding (1) et al.
  —
• Graph Neural Networks for Pressure Estimation in Water Distribution Systems (2023)
  Huy Truong et al.
  —
• Extreme Event Prediction with Multi-agent Reinforcement Learning-based Parametrization of Atmospheric and Oceanic Turbulence (2023)
  Rambod Mojgani and Daniel Waelchli and Yifei Guan and Petros Koumoutsakos and Pedram Hassanzadeh
  —
• Revisiting Tensor Basis Neural Networks for Reynolds stress modeling: application to plane channel and square duct flows (2024)
  Jiayi Cai et al.
  —
• Lattice Boltzmann simulations for soft flowing matter (2024)
  A. Tiribocchi et al.
  —
• Neural Operator-Based Proxy for Reservoir Simulations Considering Varying Well Settings, Locations, and Permeability Fields (2024)
  Daniel Badawi et al.
  —
• A Dual-Path neural network model to construct the flame nonlinear thermoacoustic response in the time domain (2024)
  Jiawei Wu et al.
  —
• Scalable physics-guided data-driven component model reduction for steady Navier-Stokes flow (2024)
  Seung Whan Chung et al.
  —