All Stories

  1. Mpox in Europe, 2022–2026: A Scoping Review of Viral Clades, Host Determinants of Severity and Antiviral Therapy
  2. Interpretable machine learning via symbolic classification of radiomic texture and morphological features for pediatric pneumonia detection from chest X-rays
  3. DNS-Calibrated Physics-Informed Neural Networks with Learnable Constants for Reynolds Number Extrapolation in Turbulent Channel Flows
  4. Prediction of neutral gas pressure in Wendelstein 7-X: Statistical analysis and machine learning
  5. Combined machine learning approaches to predict the thermal conductivity of liquid mixtures
  6. Reconstructing turbulence: A deep learning–enhanced interpolation approach
  7. Advancing Fluid Mechanics with Artificial Intelligence and Machine Learning
  8. Turbulent channel flow: A physics-informed neural network approach with embedded parameter optimization
  9. Physically Consistent Self-Diffusion Coefficient Calculation with Molecular Dynamics and Symbolic Regression
  10. Data driven prediction of the neutral gas pressure in the stellarator Wendelstein 7-X
  11. A review of deep learning for super-resolution in fluid flows
  12. Advancing super-resolution of turbulent velocity fields: An artificial intelligence approach
  13. Spatiotemporal super-resolution forecasting of high-speed turbulent flows
  14. Enhancing indoor temperature mapping: High-resolution insights through deep learning and computational fluid dynamics
  15. Comparison of super-resolution deep learning models for flow imaging
  16. Refining Flow Structures with Deep Learning and Super Resolution Methods
  17. Turbulent Micropolar Open-Channel Flow
  18. Ultra-scaled deep learning temperature reconstruction in turbulent airflow ventilation
  19. From Sparse to Dense Representations in Open Channel Flow Images with Convolutional Neural Networks
  20. Deep learning architecture for sparse and noisy turbulent flow data
  21. A deep learning super-resolution model for turbulent image upscaling and its application to shock wave–boundary layer interaction
  22. The application of data science and machine learning techniques in predicting the compressive strength of confined concrete
  23. Reassessing the transport properties of fluids: A symbolic regression approach
  24. A hybrid molecular dynamics/machine learning framework to calculate the viscosity and thermal conductivity of Ar, Kr, Xe, O and Ν
  25. Twofold Machine-Learning and Molecular Dynamics: A Computational Framework
  26. The Educational Role of Cinema in Physical Sciences
  27. Convolutional neural networks for compressible turbulent flow reconstruction
  28. Can Artificial Intelligence Accelerate Fluid Mechanics Research?
  29. Fluid Properties Extraction in Confined Nanochannels with Molecular Dynamics and Symbolic Regression Methods
  30. Impact of an inclined magnetic field on couple stress fluid flow over a stretching surface with effect of Stefan blowing, radiation and chemical reaction
  31. Adaptive thermal comfort model and active occupant behaviour in a mixed-mode apartment. A synergy to sustainability.
  32. Smoothed Particle Hydrodynamics-Based Study of 3D Confined Microflows
  33. Artificial Intelligence in Physical Sciences: Symbolic Regression Trends and Perspectives
  34. Influence of carbon nanotube suspensions on Casson fluid flow over a permeable shrinking membrane: an analytical approach
  35. Fiber-Reinforced Polymer Confined Concrete: Data-Driven Predictions of Compressive Strength Utilizing Machine Learning Techniques
  36. Thermosolutal Marangoni Convection for Hybrid Nanofluid Models: An Analytical Approach
  37. Analytical investigation of an incompressible viscous laminar Casson fluid flow past a stretching/shrinking sheet
  38. The Electrical Conductivity of Ionic Liquids: Numerical and Analytical Machine Learning Approaches
  39. Applying new methodologies in order to extrapolate new insights
  40. A combined clustering/symbolic regression framework for fluid property prediction
  41. Current Trends in Fluid Research in the Era of Artificial Intelligence: A Review
  42. Machine learning symbolic equations for diffusion with physics-based descriptions
  43. Effects of channel size, wall wettability, and electric field strength on ion removal from water in nanochannels
  44. Investigation of water desalination/purification with molecular dynamics and machine learning techniques
  45. A Water/Ion Separation Device: Theoretical and Numerical Investigation
  46. Nanoscale slip length prediction with machine learning tools
  47. An assessment of SPH simulations of sudden expansion/contraction 3-D channel flows
  48. Machine Learning Techniques for Fluid Flows at the Nanoscale
  49. Molecular Dynamics Simulations of Ion Drift in Nanochannel Water Flow
  50. Teaching cinema with machinima
  51. Molecular dynamics simulations of ion separation in nano-channel water flows using an electric field
  52. Particle-based modeling and meshless simulation of flows with Smoothed Particle Hydrodynamics
  53. Multi-parameter analysis of water flows in nanochannels
  54. Darcy-Weisbach friction factor at the nanoscale: From atomistic calculations to continuum models
  55. Friction factor in nanochannel flows
  56. Molecular dynamics simulation on flows in nano-ribbed and nano-grooved channels
  57. Fluid structure and system dynamics in nanodevices for water desalination
  58. A quasi-continuum multi-scale theory for self-diffusion and fluid ordering in nanochannel flows
  59. Fluid Flow at the Nanoscale: How Fluid Properties Deviate from the Bulk
  60. How wall properties control diffusion in grooved nanochannels: a molecular dynamics study
  61. Parameters Affecting Slip Length at the Nanoscale
  62. A novel image processing method to determine the nutritional condition of lobsters
  63. Unified description of size effects of transport properties of liquids flowing in nanochannels
  64. Surface wettability effects on flow in rough wall nanochannels
  65. Effect of wall roughness on shear viscosity and diffusion in nanochannels
  66. Effects of wall roughness on flow in nanochannels
  67. Transport properties of liquid argon in krypton nanochannels: Anisotropy and non-homogeneity introduced by the solid walls
  68. Variation of Transport Properties Along Nanochannels: A Study by Non-equilibrium Molecular Dynamics