Conference 2025 Files To Download

AIFLUIDs Programme Wednesday.pdf

AIFLUIDs Programme Thursday.pdf

AIFLUIDs Programme Friday.pdf

Posters.pdf

AIFLUIDs2025_Conference Information.pdf

Conference 2025 Proceedings

Reinforcement Learning and flow control
📄 D. Drikakis, F. Sofos, Deep Learning for Flow Imaging and Spatiotemporal forecasting.pdf
📄 Y. Chen, Y.Yang, Deep reinforcement learning for tracking a moving target in jellyfish-like swimming.pdf
📄 D. Drikakis, Nicholas Christakis, Integrating Unsupervised Learning with Computational Fluid Dynamics.pdf
📄 F. Mashayek, P. Thoguluva Rajendran, F. Mashayek, Deep Reinforcement Learning of Active Flow Control Policies for Pitching Moment Control.pdf
📄 P. Suárez Morales, F. Alcántara-Ávila, A. Miró, J. Rabault, B. Font, O. Lehmkuhl, R. Vinuesa, Active flow control for drag reduction through multi-agent reinforcement learning on a turbulent cylinder at ReD=3900.pdf
📄 G. Maria Cavallazzi, L. Guastoni, R. Vineusa, A. Pinelli, Manipulation of the turbulent wall cycle via multi-agent deep reinforcement learning.pdf
📄 Bo YIN, S. Huang, Q. Cao,D. Guo, G. Yang, Research on intermittent swimming based on deep reinforcement learning.pdf
📄 F. Sofos, A. Palasis and A. Liakopoulos, Data- and physics-driven analysis of turbulent channel flows: Insights from DNS and Deep Learning.pdf
📄 L. Cordier, T. Singh R. Fablet, Active flow control using neuroevolution guided deep reinforcement learning.pdf
📄 M. Masdari, S. Mousavi, M. Jebelli, Accelerating Deep Reinforcement Learning- based Active Flow Control on Analogous Geometries through Transfer Learning.pdf
📄 R. Montalà Sales, B. Font, P. Suarez, J. Rabault, O. Lehmkuhl, R. Vinuesa and I. Rodriguez, Deep Reinforcement Learning for Active Flow Control Around a Flow-Separated Wing.pdf
📄 K. Klapcsikm, Acoustic Cavitation Bubble Control by Reinforcement Learning Using GPU Accelerated Environment Simulations.pdf
📄 G. Torres Marques Vidal, R. Mário Figueiró Vargas, K. Ruschel, A Data-driven Approach to Simulate Particle laden Gravity Currents Time Evolution with Convolutional Neural Networks and Long Short-term Memory.pdf
📄 F. Sobieczky, A. Lopez, E. Dudkin, C. Lackner, M. Hochsteger, B. Scheichl, H. Sobieczky, Reinforcement Learning for Accelerated Aerodynamic Shape Optimization.pdf

Inference, Sensing, Inverse Modeling
📄 Y. Yugeta, Y. Hasegawa, Automatic search for an effective cost function in the suboptimal control of cylinder wake by genetic programming.pdf
📄 P. Shaowu, N. Somasekharan, Y. Cao, F. Kopsaftopoulos, M. Amitay, Deep Koopman Sensing.pdf
📄 M. Masuda, Y. Tamura, Prediction of pressure field of incompressible flow using CNN.pdf
📄 Z. Sun, S F Xu, D L Guo, G W Yang, On the preprocessing of physics-informed neural networks: How to better utilize data in fluid mechanics.pdf
📄 M. Ole Loft, H. Schwarz, T. Rung, Data-based surrogate modeling to reconstruct pressure fields of ships in arbitrary sea-states.pdf
📄 C. Kim, S. Oh, H. Choi, Prediction of a scalar source location from remote sensors in turbulent flow using machine learning .pdf
📄 S. Kramer, L. Souverein, L. Agostini, G. Waxenegger-Wilfing, S. Schlechtriem, Flow field reconstruction of rocket engine turbine on sparse sensors .pdf
📄 D. Rigutto, M. Alfonso Mendez, M. Ratz, Anisotropic and Multi-resolution RBFs for mesh-less Data Assimilation of scattered data .pdf
📄 M. Alfonso Mendez, M. Ratz; S. Ahizi; A. Parente, A Framework for Meshless Data-Driven Decompositions with RBF-Based Inner Products.pdf
📄 B. Sun, S. Cai, Q. Du, L. Lu, R. Wang, S. Wang, L. Xie, R. Xiao, X. Liu, J. Zhu, Reconstruction of Fields with Physics-Informed Neural Networks Based on Optimal Sensor Placement.pdf
📄 N. Chenjia, W. Zhang, Multi-source Heterogeneous Aerodynamic Data Fusion Neural Network Embedding Reduced-dimension Features.pdf
📄 M. Staggl, S. Posch, W. Sanz, Solving Inverse Fluid Flow Problems with Differentiable Reduced Basis Models.pdf
📄 S. Anagnostopoulos, G. Rovas, L. Aslanidou, Fast personalized arterial flow inference with physics-informed neural networks and minimal non-invasive data.pdf
📄 J. Mairal, J. Orera, J. Murillo, P. Garcia-Navarro, Solving the inverse problem of arterial stiffness through Physics-Informed Neural Networks.pdf
📄 S. Posch, M. Staggl, W. Sanz, Differentiable Simulation for Inverse-like Fluid Flow Problems.pdf
📄 H. Goordoyal, A. Barnes, A. Cookson, K. Fraser, Improving the accuracy of data-driven multi-fidelity neural networks applied to computational fluid dynamics using adaptive sampling.pdf
📄 H. Haifeng, He B.,Cai G., Zhang B., Weng H., Wang W, Exploring the potential applicability of deep learning methods in computing wall distributed aerodynamics and thermal effects in rarefied flows.pdf
📄 J. Kou, Y. Wang, W. Zhang, Knowledge-based and Data-Driven Causal Analysis for Galerkin Models Exemplified by Turbulent Shear Flows.pdf

Multi-scales
📄 M. D. Mays, C. Marsh, S. Rezaeiravesh, A. Revell, Application of multi-fidelity Gaussian Process modelling to the Common Research Model - High-Lift geometry for use in digital environments.pdf
📄 M. Laudato, L. Manzari, K. Shukla, Neural Operator Modeling of Platelet Geometry and Stress in Shear Flow.pdf
📄 N. Tonioni, L. Agostini, F. Kerhervé , L. Cordier, R. Vinuesa, VIVALDy: AI-Driven Low-Order Modeling of Vortex-Induced Vibrations via β-Variational Autoencoders, Transformers, and Adversarial Training.pdf
📄 M. Yang, H. Liu, D. F. del Pozo; T. Chen, Development of an AI Surrogate Model Trained on CFD-ASM Results for Predicting Pollutant Degradation and Dispersion in Ecological Wetlands.pdf
📄 P. Stamatopoulos, G. Kanellis, T. Lyras1, D. Stefanitsis, N. Nikolopoulos, Numerical Simulation of Industrial-Scale Fluidised Bed Reactors Using an Artificial Neural Network EMMS Drag Model.pdf
📄 C. Brunelli, B Janssens, K. Hillewaert, M. Runacres, Incremental SVD-Based Compression for Unsteady Adjoint Fields.pdf

PDE Solvers
📄 A. A Michailidis, M. Kiffner, Tensor Networks in Machine Learning and PDEs.pdf
📄 J. Meurisse, B. Dias, AI-Enhanced Computational Tools for Entry Systems Modeling.pdf
📄 P. Beck, T. M. Schneider, Guessing and converging periodic orbits in fluid flows with machine learning.pdf
📄 M. Furuya, M. Kobayashi, F. b. N. Sarbaland, D. TANAKA, R. FUJITA, Explainable AI to Explore Physics Behind Fluid Mechanics.pdf
📄 R.M. Barron, V. Shah, L. Fadia, M. Hassanzadeh, Accelerating PDE Simulations by Integrating Machine Learning and Finite Difference Technologies.pdf
📄 J. H. HONG, S. Y. HONG, J. KIM, Don-Gwan AN, S. SONG, PINGS-X: Physics-Informed Normalized Gaussian Splatting with Axes Alignment for Fast and Accurate Super-Resolution of Phase-Contrast MRI.pdf
📄 P. Perdikaris, S. Wang, S. Sankaran, H. Wang, kSimulating Fluid Flows with Continuous Vision Transformers.pdf
📄 J. Hu, Z. Lu, Y. Yang, Generative prediction of flow fields around an obstacle using the diffusion model.pdf
📄 L. Berthet, B. Blais, F. P. Gosselin, Finite Element Neural Network Method for Navier-Stokes Equations.pdf
📄 H. Ramachandran, A. P. Toshev, S. Schmidt, N. A. Adams, On Diffusion-based Graph Neural Networks for Lagrangian Fluid Simulations.pdf
📄 J..L. Cummings, C. Fernandes, F. Dong, M. A. Alves, M. S. N. Oliveira, Exploring the Use of Feed-Forward Multilayer Perceptron Networks for Modeling the Non-Linear Behaviour of Viscoelastic Fluids.pdf
📄 D. Dapelo, J. Bridgeman, FluidGPT-1: a proof-of-concept model for attention-based flow pattern generation in CFD.pdf
📄 O. E. Coker, P. K. Jimack, A. Khan, H. Wang, Temporal Weighted Loss Curriculum Learning for Neural Operators.pdf
📄 X. Wang, C. Ning, Z. Liu, W. Zhang, A Brief Survey on Data-driven Convergence Acceleration Methods in Computational Fluid Dynamics.pdf
📄 V. Šálený, A. Mikeš, T. Pavlůsek, M. Kubíček, David Among HPC Goliaths: Redefining Cost-Performance-Energy Balance.pdf
📄 Z. Zhang, P. K. Jimack, H. Wang, A Graph Neural Network for Guiding Adaptive Finite Element Mesh Refinement in Transient Flow Simulation.pdf
📄 A. Ryabov, S. Shumilin, V. Naumov, N. Yavich, S. Ranu, N. M. A. Krishnan, E. Burnaev, V. Vanovskiy, Self-Supervised Computational Graph Coarsening for Accelerating Two-Dimensional Subsurface Flow Simulations.pdf
📄 Z. Wang, W. Zhang,S. Song, Euler equation embedding Double-series Residual neural Network for aerothermal modelling and prediction.pdf

Turbulence
📄 S. Molina, A. Cremades, S. Hoyas, J. Cardesa, F. Chedevergne, R. Vinuesa, On the accurate identification of wall-bounded coherent structures via SHAP methodologies for 2D turbulent fields.pdf
📄 Z. Zhou, X. Zhu, Deep Reinforcement Learning for enhancing heat transfer in turbulent convection.pdf
📄 B. Z. Han, W. X. Huang, C. X. Xu, Restricted nonlinear model for reinforcement-learning-based control of turbulent channel flow.pdf
📄 M. Castelletti, M. Quadrio, Automatic turbulence modelling.pdf
📄 C. Cho, H. Choi, A dynamic recursive neural-network-based subgrid-scale model for large eddy simulation.pdf
📄 M. Ihme, J. Z. Ho, B. Yeo, B. Akoush, BLASTNet: Accessible community-involved big data as key-enabler for Fluids-AI.pdf
📄 Z. Lakdawala, H. Kassem, W. Nadeem, On training of data-driven and physics driven neural networks for wind farm planning.pdf
📄 M. S. Ghazijahani, C. Cierpka, Temporal modeling of turbulence via echo state networks.pdf
📄 B. Eiximeno, M. S. Agudo, A. Miró, Rodriguez, R. Vinuesa, O. Lehmkuhl, Towards Deep-Learning Based Probabilistic Closures for Algebraic Surrogate Models of Turbulent Flows.pdf
📄 C. Roques; G. Dergham, X. Merle, P. Cinnella, Improving the reliability of turbulent flow predictions using Clustered Model Aggregation.pdf
📄 T. Li, M. Buzzicotti, A. S. Lanotte, F. Bonarccorso, L. Biferale, L. Centurioni, Diffusion Models for Reconstruction of Eulerian and Lagrangian Turbulent Data.pdf
📄 Z. Li, H. Dou1; S. Fang, W. Han; L. Yang, Efficient simulation and assimilation of turbulent flow using diffusion transformer.pdf
📄 L. Saverio, M. A. Bucci, C. Content, D. Sipp, Differentiable Learning for Turbulence Modeling: A Gradient-based framework in RANS Simulations.pdf
📄 K. Ando, R. Bale, A. Kuroda, M. Tsubokura, Neural Network-Based Parametric Model Reduction for Predicting Turbulent Flow for Different Vehicle Geometries.pdf
📄 H. Tofighian, J. Denev, N. Kornev, Super-Resolution Reconstruction of Particle-Laden Turbulent Flows Using Conditional Deep learning.pdf
📄 G. Van Essche, T. Haas, J. Decuyper, T. De Troyer, M. C. Runacres, Data-Driven Modelling of the Wake of a Pitching Porous Disk.pdf
📄 M. Reissmann, Y. Fang, A. S. H. Ooi, R. D. Sandberg, Accelerating Evolutionary RANS Turbulence Modelling through Transformer-based Augmentation.pdf
📄 T. Berthelon, A. Mahdi, G. Balarac, Enhanced LES Predictions Through Multi-Fidelity Integration Using High-Fidelity Point Selection Strategy.pdf
📄 J. Yang, S. Dalpke, A. Stroh, Physics-Informed Adversarial Network for prediction of turbulent flow over rough surfaces.pdf
📄 X. Shan, W. Zhang, Progressive Data-Driven modification for Spalart-Allmaras Turbulence Modeling.pdf

Industrial & Applied ML in Fluid Systems
📄 H. Schwarz, P. P. Lin, Jens-Peter M. Zemke, T. Rung, Convolutional Autoencoder based Prediction of Ditching Loads with Disentangled Latent Space.pdf
📄 K. Liu, C. Atkinson, S. Badia, J. Soria, Data-driven reduced order modelling of a bushfire analogy using variational autoencoders.pdf
📄 V. Vijayarangana, H. G. Im, Understanding the latent manifolds of autoencoders using information geometry for the stiff dynamical systems.pdf
📄 S. A. Ahizi, M. A. Mendez, Meshless POD for Sloshing Modes identification in Non- Canonical Tank Geometries.pdf
📄 G. Barragán, A. Sengupta; 1 R. Abadía-Heredia, A. Hetherington, J. Garicano-Mena, S. Le Clainche, Hybrid machine learning reduced order models for efficient forecasting and data generation in fluid dynamics databases.pdf
📄 T. Tang, Y. Chen, R. Cao, W. Mostert, P. H. Taylor, M. L. McAllister, A. H. Callaghan, T. A. A. Adcock, Discovering Boundary Equations for Wave Breaking using Machine Learning.pdf
📄 X. Luo, J. Yin, S. Kuhn, Implementation of a data-driven model for mesh-induced error corrections in CFD simulations of stirred tank reactors.pdf
📄 N. I. Prasianakis, H. Peng, M. Baur, R. Boiger, A. Mokos, S. V. Churakov, Enhancing Reactive Transport Simulations with Machine Learning, Adaptive Algorithms, and High-Performance Computing.pdf
📄 R. Sato, E. Nakahama, Y. Nabae, H. Gotoda, Early detection of thermoacoustic instability in a swirl-stabilized turbulent combustor using a noise-induced dynamical system and a deep neural network.pdf
📄 D. Huang, L. Pastur, N. Deng, B. Noack, Least-order parametric modeling of an incompressible open cavity flow.pdf
📄 D. Ponkratov, M. Bucci, K. Moran, N. MacLennan, M. Wheeler, How does AI solve the challenge of vessel draught mark readings.pdf
📄 J. Awan, M. Wheeler, D. Ponkratov, Leveraging Generative AI for Simulation-Driven Optimization of Ship Hull Forms.pdf
📄 D. Aubagnac-Karkar, C. Mehl, Accounting for variable time-steps in Neural Network accelerated kinetic computations.pdf
📄 J. Matheus, F.-J. G. Ortiz, Application of Reduced-Order Models and Machine Learning to Enhance Downlink Communication in Rotary Steerable Systems for Oil & Gas Drilling.pdf
📄 M. Popovac, D.P. Gupta, T. B. Gohil, 3D flow field reconstruction using ANN as a surrogate model of the fluid flow within rectangular wall-bounded enclosures.pdf
📄 A. Junk, J. M. Winter, S. Schmidt, N. A. Adams, Fourier Neural Operator Surrogate Modelling of Lattice-Boltzmann Simulations.pdf
📄 E. Dikbaş, Local Spectrum Analysis of Hybrid RANS-LES Data Using Dynamic Mode Decomposition for a Body-Wing- Tail Airframe.pdf
📄 M. Russo, D Petronio, D. Lengani, D. Simoni, F. Bertini, A POD-based Autoencoder for Detailed Loss Characterization of Low Pressure Turbine Blades.pdf
📄 De Paola, Camussi, Di Marco, Stoica, Capobianchi, Marongiu, Beretta, Paglia, Application of Machine and Deep Learning Techniques for Acoustic Load Predictions on the VEGA-E Launcher.pdf
📄 M. Mesgarpour, S. Sadrizadeh, K. G. Kyprianidis, R. B. Fdhila, Numerical and SVM-based Analysis of CO and CO2 Production in the Slag Fuming Furnace: Influence of Operational Conditions.pdf
📄 W. G. Habashi, Reduced Order Models: A Step Toward Aircraft In-Flight Icing Certification by Analysis.pdf
📄 A. Pela, M. Marconcini , A. Arnone, A. Agnolucci, E. Belardini, R. Valente ,A. Grimaldi, L. Toni, Convolutional Neural Network Approach for Impeller Blade Loading Inference.pdf
📄 Z. Ziheng, H. Bijiao, Z. Baiyi, W. Huiyan, W. Weizong, Vacuum plume field reconstruction method for variable thrust engines based on deep learning.pdf
📄 A. M. Dolatabadia, G. Petruccellia, A. Grönmana, T. Turunen-Saaresti, Developing Supervised Machine Learning to Predict Supercritical CO2 Characteristics.pdf
📄 Y. Le Guennec, Y. Blacodon, S Tollis, T. Defoort, J. V. Aguado, D. Borziachello, AI-CFD Based Analysis for Propellant Management in Cryogenic Tanks.pdf
📄 I. Hubbard, T. Melissaris, Data & Physics-Driven Graph Convolutional Networks for Rapid Cavitation Energy Predictions on Marine Propeller Blades.pdf
📄 N. Hajaliakbari, D. Head, O. Harlen, The Effects of Temperature on Hydrodynamic Interaction of Sedimenting Semi-Flexible Brownian Fibres.pdf
📄 B. Einberger, R. Pöschl, A. Ennemoser, Data-Driven Ejector Layout: Integrating High-Fidelity CFD, Machine Learning Surrogate, and Genetic Algorithms.pdf
📄 B. LIU, L. ZHANG, Y. LIU, X. LI, Effects of chock and shock save effect on aerodynamic load and temperature of subsonic vacuum tube maglev train.pdf
📄 C. Pliakos, G. Efrem, D. Terzis, P. Panagiotou, Data-driven skin friction estimation for UAV wings in subsonic flows.pdf
📄 W. Suo, W. Zhang, An improved finite difference method enhanced by deep neural networks.pdf
📄 H. A. Abderrahmane, M. Tembely, Asymptotic Model for Flow in Heterogeneous Media.pdf
📄 S. J. Baker, S. Goswami, X. Fang, F. C. P. Leach, Vector-based loss functions for turbulent flow field inpainting.pdf
📄 S. Fang, Z. Li, Qi. Fu, W. Han, L. Yang, Generative Modelling of Temperature Field Evolution from Sparse Pressure Measurement.pdf
📄 K. Tsujimoto, T. Tanoue, T. Ando, M. Takahashi, Diffusion suppression control of a free jet at a medium Reynolds number with transverse vibration of inflow.pdf
📄 B. An, S. Chen, M. Nohmi, Mode Analysis of Unsteady Flow in Centrifugal Pumps.pdf
📄 C. Lefkiou, F. (Phoevos) Koukouvinis, S. Chatzis, Flow field prediction around 2D bluff bodies.pdf

Uncertainty quantification
📄 F. -Javier Granados-Ortiz, C. Airiau, J. Ortega-Casanova, Spatial Propagation of Uncertainty in the Stability Analysis of Supersonic Jets with Stochastic Base-flows as Reduce-Order Models.pdf
📄 J. Sena Sales Junior, R A Barreira, P T T Esperança, Bayesian Network Model for FPSO Offset Prediction Using LLM Under Mooring Line Failure.pdf
📄 A. Eidi, T Buchanan, L Jiang and R P Dwight, Physics-Guided Bayesian Neural Networks for Zonal Corrections and Uncertainty Quantification in Separated Flows.pdf
📄 C. CHOI, J. Heo, S. Lee, Preliminary Inverse Uncertainty Quantification of Post-CHF Phenomena Using SPACE Code.pdf
📄 X. Zhang, Z Zhang, Z Sun, J Q Shi, G J Nathan and R C Chin, Sensitivity and uncertainty analyses in deeplearning-augmented unsteady Reynoldsaveraged Navier–Stokes turbulence modelling for particle-laden jet flows .pdf
📄 H. Jami, F. Brännström, Machine Learning approaches for uncertainty quantification of a CFD Pyrolysis Model.pdf

Dimensionality Reduction and Reduced Order Models
📄 S. Maejima, S. Kawa, Interpretation of the machine learning attention matrix for flow analysis.pdf
📄 H. Muhammad Azam, A. Procacci, A. Coussement, A. Parente, Constrained reduced-order modelling of reacting flows .pdf
📄 N. Kumar, F. Kerhervé, L. Agostini and L. Cordier, Deep learning of SPOD time-domain coefficient dynamics for reduced-order modeling of street canyon flow.pdf
📄 F. Ahsan Khan, Navrose, Shallow Decoder for low latency flow reconstruction using limited measurements.pdf
📄 G. Y. Cornejo Maceda, Q. L. Li-Hu, A. Ianiro, S. Discett, Manifold of clusters for complex flows.pdf
📄 A. R. Asensio, G. Y. C. Maceda, L. Marra, A. Meilán-Vila, B. R. Noack, A. Ianiro, S. Discetti, Feature-based manifold model for fast actuated transients.pdf

Heat transfer
📄 J. Marie, C. Allery, C. Béghein, V. Melot; M. Balin, Simulation of Heat Exchanger Transient Phases Using Non-Intrusive Parametric ROMs.pdf
📄 M. Zhang, Z. Li, Z. Yu, Machine Learning-Powered Prediction Framework for Household Heating Demand.pdf
📄 M. Hughes, Z. Chen, A. S. Lobasov, M. Bucci, C. N. Markides, On the Use of Artificial Intelligence to Accelerate the Processing and Analysis of High-Throughput High-Resolution Experimental Boiling Heat Transfer Data.pdf
📄 A. Kladas, I. Alonistiotis, Neural Network-Based Estimation implemented in a particular Dielectric Oil Spray Electric Motor Cooling Technique.pdf
📄 S. Poovathingal, Vijay B M, A supervised learning model to capture ablation of heat shields.pdf
📄 G. Krishnamoorthy, L. Holtshouser, K. Viswanathan, Predicting Heat Transfer Rates in Laminar Supercritical Flows using Machine Learning Approaches .pdf

Turbomachinery and airfoils
📄 D. Rubini, B. Rosic, ChemZIP: Accelerated Modeling of Complex Aerothermochemical Interactions in Novel Turbomachines for Sustainable High-Temperature Chemical Processes.pdf
📄 K. Kellaris, Y. Ding, M. Manolesos, On the use of Hidden Markov Models to investigate airfoil stall dynamics.pdf
📄 A. N. Rao, M. Carta, T. Ghisu, S. Shahpar, F. Montomoli, Computer Vision-Based Performance Forecasting for Turbomachinery.pdf
📄 G. Goinis, S. Satcunanathan, M. Aulich, C. Voß, Assessing the Use of Transformer AI Models as CFD Substitutes in Airfoil Optimization.pdf
📄 S. G. Paquette, E. Laurendeau, D. Vidal, Rotorcraft full state-space AI-accelerated predictions of aerodynamic coefficients.pdf
📄 W. Cao, W. Zhang, Solving high-dimensional parametric flow problems around airfoils using neural network solvers.pdf

Physics-informed models
📄 A. Zargaran, U. Janoske, Modeling Flow Dynamics in Rotor-Stator Mixers using Data-Free Physics-Informed Neural Networks.pdf
📄 T. Gammaidon, A. Bertrami, J. Zembi, M. Battistoni, Towards Efficient Heat Transfer Simulations: A Comparative Study of PINN vs. CFD.pdf
📄 B. Thurow, D. Kelly, P. Mouaikel, FluidNeRF: a Machine Learning Based Approach for Physics Informed 3D Flow Tomography.pdf
📄 Y. Jiao, A. Bußmann, S. J. Schmidt, N. A. Adams, Ultrasonic Field Modeling and Prediction Using Physics-Informed Machine Learning.pdf
📄 R. Qiu, J. Wang, Z. Zhang, Y. Wang, A hybrid physics-enhanced turbulent model for high-fidelity flow predictions.pdf
📄 H. Hua, B. Huang, S. He, H. Han, Z. Zuo, S. Liu, Solve advection-diffusion-Langmuir adsorption processes in 2-D oscillatory flows using residual physics-informed neural networks.pdf
📄 E. Khoshbakhtnejad, H. Sojoudi, Predicting Coefficient of Restitution of Ice Particles Impacting Surfaces: A Machine Learning-Based Approach.pdf
📄 J. Florido, P. K. Jimack, A. Khan, H. Wang, Adaptive collocation point sampling strategies for physics informed neural networks.pdf
📄 M.J. S. Horne, P. K. Jimack, A. Khan, H. Wang, Hard constraint projection in a physics informed neural network.pdf
📄 M. Sheikholeslami, S. Salehi, W. Mao, A. Eslamdoost, H. Nilsson, Comparative Evaluation of Periodic Boundary Condition Approaches in PINNs.pdf
📄 J. Orera, J. Murillo, Inference of lumen area and PWV in a healthy adult thoracic aortic network using PINNs.pdf
📄 L. Lu , Y. Zou, J. Wang, X. Deng, MBPINN: Mesh-based Physics-Informed Neural Networks for Global and Local Hyperbolic Conservation.pdf
📄 L. Ugur, B. Y. Zhou, Data-Driven Stochastic Turbulence Generation via Physics-Informed Field Inversion Machine Learning.pdf
📄 G. M. Shutov, D. I. Akhmetov, E. V. Burnaev, V. V. Vanovskiy, Physics-informed local-global underground fluid flow modeling with multiple sinks.pdf
📄 F. B. Poor, D. L. M. Hui, N. Plückhahn, M. Rom, K. Schmitz, Physics-Informed Neural Networks for Non-Newtonian Lubricated Contacts: Advancing AI-Driven Fluid Mechanics.pdf
📄 D. Igarashi, S. Kumagai, Y. Yokoyama, Y. Jingzu, M. Horie, Y. Tagawa, 3D Fluid Stress Field Reconstruction from Flow Birefringence: Physics-Informed Convolutional Encoder-Decoder Approach.pdf
📄 M. Dreisbach, E. Kiyani, J. Kriegseis, G. Karniadakis, A. Stroh, Convolutional feature-enhanced physics-informed neural networks for reconstructing two-phase flows.pdf
📄 Y. Wang, S. Shelyag, J. Schluter, Physics-informed Transformer-based Neural Operator for Parametric Super Resolution of Turbulent Channel Flows from Spatially Sparse Data.pdf
📄 V. Kitsios, L. Cordier, T. J. O' Kane, Data-driven and physics-constrained reduced-order model of the global climate.pdf

Multi-phase
📄 J. L. P. Costa, Z. Kokkinogenis, J. B. L.M. Campos, M. C. F Silva, Integrating Super-Resolution and Segmentation for Enhanced Bubble Identification in Multi-Phase Flow.pdf
📄 T. Noda, Y. Motozono, K. Okabayashi, A priori and a posteriori tests of data-driven cavitation model.pdf
📄 J. Hwanga, C. Baea, Ioannis K. Karathanassis, P. Koukouvinisc, M. Gavaises, Paradigm Shift in Propulsion System Development: The Role and Potential of AI.pdf
📄 M. Cutforth, S. Mirjalili, Autoencoders for reconstructing interfacial multiphase flows.pdf
📄 A. Goharzadeh, I. Alsafadi, H. A. Abderrahmane, Data-Driven Model for Swirling Jet Atomization.pdf
📄 J. S. Sales Junior, E. F. De Paula Filho, Use of LLM models on the Computational Hydrodynamic evaluation of floating bodies in Waves.pdf
📄 S. Zehisaadat, S. J. Schmidt, N. A. Adams, Fine-tuning a foundation model on multiphase problems.pdf

Experimental data
📄 B. Akoush, G. Vignat, R. Finley, W. Tong Chung, M. Ihme, Experimental demonstration of deep reinforcement learning adaptive control of thermoacoustic instabilities in a lean-premixed methane hydrogen air combustor.pdf
📄 T. Käufer, J. D Toscano, Z. Wang, M. Maxey, G. E. Karniadakis, C. Cierpka, Inferring temperature from velocity data in turbulent thermal convection by PIML Concept and experimental validation on simultaneously measured 3D temperature.pdf
📄 R. Naramura, C. Abe, Y. Sasaki, T. Nonomura, Real-time feedback control of flow field behind a cylinder using sparse processing PIV and plasma actuators.pdf
📄 A.M. Ali, S.A. Mäkiharju, PINNs Augmented X-Ray Particle Velocimetry.pdf
📄 Z. Wu, J. Lin, Y. Zhang, Y. Zhou, Comparative experimental study on jet mixing enhancement using deep reinforcement learning and genetic programming.pdf
📄 Ch. Brücker, R. Glick, V. Sunthareswaran, S. Ponnusami, Underwater object tracking using whisker-type flow sensors and Machine Learning.pdf

Optimisation and Design
📄 K. Evgenia, S. Florian, D. Erika, R. Michael, P. Bhargav, M. Thomas, AI assisted design of hydraulic turbine components and plausibility check of experimental data based on anomaly detection techniques.pdf
📄 H. von Schöning, M. Liu Perkins, T. Wolf, An aerodynamics copilot for automotive exterior designers.pdf
📄 C. CalascibettaL. Giraldi, Z. El Khiyati, J. Bec, Harnessing Swarms to Optimize Transport of Interacting Active Particles.pdf
📄 U. Adia, A. Khan,A. Sleigh, H. Wang, Machine Learning Based Intelligent CFD Surrogates for Interactive Design Exploration of Built Environments.pdf
📄 G. Klavaris, A. Gantner, T. Danninger, W. Bower, A Data-Driven Turbulence Modelling Framework based on Machine Learning for Industrial Aero-Engine Design.pdf
📄 S. Watanabe, Y. Hasegawa, Shape optimizations of fluid flow around a circular cylinder by using PINNs.pdf
📄 M. Schouler, C. Matar, X. Gloerfelt, A. Belme, P. Cinnella, Machine learning-based aerodynamic optimization with extreme fidelity cost imbalance.pdf
📄 D. A. Bezgin, A. B. Buhendwa, S. J. Schmidt, N. A. Adams, Differentiable Fluid Dynamics for Shape Optimization in Compressible Flows.pdf
📄 M. Giovannini, M. Silei, J. Bellucci, E. Spano, B. Francesco, Data-Driven Multi-Fidelity Framework for Real-Time Optimization of LPT Blades.pdf
📄 G. Bletsos, A. Hassan, M. Arian Maram, T. T. Nguyen, M. Palm, T. Rung, Adjoint-based shape optimization of an OSV hull using a VAE-assisted propulsion surrogate model.pdf
📄 J. Rottmayer, L. Chen, N. R. Gauger, Bayesian Neural Networks for Surrogate-based Optimization in Aerodynamic Shape Optimization.pdf
📄 A. Mokos, Y. Sato, B. Niceno, S. V. Churakov, N. I. Prasianakis, Eulerian Multiphase CFD Model Optimisation and Sensitivity Analysis for Nuclear Reactor Fuel Assembly Simulations.pdf
📄 E.A. Arens, P. Mas, M. Wheeler, D. Ponkratov, An integrated workflow to train and seamlessly leverage AI models for vessel design optimisation and performance prediction.pdf
📄 M. G. Kontou, V. G. Asouti, X. S. Trompoukis, K. C. Giannakoglou, ML Surrogate for a CAD Model - Application to the Shape Optimization of the DLR-F25 Transport Aircraft.pdf
📄 F. Sobieczky, E. Dudkin, A. Lopez, C. Lackner, M. Hochsteger, B. Scheichl, Interpretability vs. descriptive power of parametrizations in AI-assisted Aerodynamic Shape Optimization.pdf
📄 I. Kim, J. Chae, H. Bae, D. You, Automation of the CFD-based design process for turbomachinery blades using deep reinforcement learning.pdf
📄 M. Kubíček, A. Guerrero, A. Trčka, D. Franco, P. Pantalone, V. Šálený, POMAI: Probabilistic Optimization Modelling using Artificial Intelligence.pdf
📄 F. J. Adams, M. P. Brown, E. Sorguven, M. Chughtai, Accelerating Low Noise Axial Fan Design Space Exploration: A Surrogate Modelling Framework for Multi-Objective Optimisation.pdf
📄 D. Lee, K. Shim, S. Choi, Transonic Flow Analysis using Vision based Model Order Reduction and Application to 3D Wing Shape Optimization.pdf

Digital Twins
📄 S. Randino, L. Schena1,, N. Coudou, E. Garone, M. A. Mendez, Real time data assimilation for the digital twinning of wind farms.pdf
📄 L. Schena, P. Marques, R. Poletti, S. Ahizi,, J. van den Berghe, M. A. Mendez, Reinforcement Twinning: from digital twins to model-based reinforcement learning.pdf
📄 G. Georgopoulos, A. Pytharouliou, P. Delizisis, A. Kopanidis, DIGITAL TWIN: Spectral Analysis Verification of Payload Handling Operation on Vessels.pdf
📄 H. Abedini; S. Bennici, A. Gering, Development of a Digital Twin for Alkaline Water Electrolyzers.pdf
📄 Y. Lecomte, M. Ratz, L. Schena, A. Vardanyan, M. A. Mendez, Reinforcement twinning for attitude control of multirotor drones: an experimental proof of concept.pdf
📄 R. Poletti, L. Schena, L. Koloszar, J. Degroote, M. A. Mendez, A hybrid model-based-model-free approach for flight control of flapping wing drones using reinforcement twinning.pdf

Combustion, Flame, Spray
📄 G. Corlùy, K. Zdybał , X. Wen, L. Berger6 , H. Pitsch6, A. Parente, Progress variable optimization in a hydrogen flame for reduced-order modeling using an encoder-decoder.pdf
📄 M. Kawai, S. Esaka, N. Sugimura, A. L. Pilla, R. Kurose, Deep learning-based prediction of flashback of swirling hydrogen-air flame.pdf
📄 R. Yu, E. Hodzic, Koopman-Inspired Operator Learning for Intrinsic Flame Instabilities.pdf
📄 B. Delhom, C. Habchi, O. Colin, J. Bohbot, Development of a multi-species real fluid modelling approach using a machine learning method, application to combustion.pdf
📄 T. Seshime; T. Haga; H. Gotoda; Y. Nabae; R. Kurose, Complex-network analysis of high-frequency combustion instability in a single element combustor for liquid rocket engines.pdf
📄 C. López, B. Herrmann, R. Demarco, F. Escudero, Sparse Sensor Placement and Physics-Informed Neural Networks for Temperature and Velocity Fields Reconstruction in Axisymmetric Flames.pdf
📄 J. Wu, J. Liu, S. Zhang, Y. Wu, X. Li, Physics-informed neural networks to solve 1D laminar flames.pdf

Environmental and atmoshperic
📄 K. Nowakowska, D. Parker, S. Tobias, L. Tomassini, Echo State Networks for Nowcasting a Simplified Model of Atmospheric Convection.pdf
📄 J. Park, C. Lee, AI-Driven High-Resolution Precipitation Nowcasting: Insights into Atmospheric Fluid Dynamics and Spatiotemporal Correlations.pdf
📄 A. de Villeroché, V. Le Guen, Rem-Sophia Mouradi, P. Massin, M. Bocquet, A. Farchi, S. Cheng, P. Armand, MeshGraphNets for 3D atmospheric flow in Urban Environment for Atmospheric Dispersion.pdf
📄 Y. Kanishima, K. Saito; R. Ito, T. Ueda, Y. Taniyama, K. Araki, A. Kyomasu, Comparison of strong wind forecasts by fluid analysis-based and AI-based methods.pdf
📄 V. Kossov, O. Fedorenko, M. Tuken, Peculiarities of occurrence of concentration gravitational currents caused by mechanical equilibrium loss of a multicomponent system containing greenhouse gases.pdf

Machine Learning
📄 E. Franz, H. Wei, L. Guastoni, N. Thuerey, PICT: Adaptive GPU Accelerated and Differentiable Fluid Simulation for Machine Learning.pdf
📄 B. Sundaravadivelan; A. Scotti, Reliable Collision Predictions in Chaotic Flows with Sequence-to-Sequence Models.pdf
📄 M. Tütken, J. Winter, N. A. Adams, Hybrid Machine Learning-Simulation Workflows for Efficient Fluid Simulations.pdf
📄 B. Sapkota, H. Mettelsiefen, M. Moaven , S. H. Halder, V. Raghav, B. Thurow, Machine Learning-Enhanced Light-Field Fluid-Structure Interaction Diagnostics.pdf
📄 M. A. Maram , A. Hassan, T. T. Nguyen, H. Schwarz, G. Bletsos, D. Bendl, M. Palm, T. Rung, Development of a data-driven propulsion surrogate model using Machine Learning methods.pdf
📄 J. Reuterb , H. Elmestikawya, S. Mostaghimb, B. van Wachem, Drag modelling for flows through assemblies of spherical particles with machine learning: A comparison of approaches.pdf