Predicting EHL film thickness parameters by machine learning approaches

• Method for Real-Time Monitoring of the Lubrication Regimes in Dynamically Loaded Radial Sliding Bearings Using Physics-Informed Neural Networks (PINNs)

  2026 Lubricants article Engineering Tribology and Lubrication Engineering Open access

  0

  citations
• Accelerating Computation of Sealing Deformation—A Physics-Informed Neural Network Framework for Hyperelastic Deformation

  2026 Lecture notes in computational science and engineering conference-paper Physics and Astronomy Model Reduction and Neural Networks

  0

  citations
• Extrapolation of cavitation and hydrodynamic pressure in lubricated contacts: a physics-informed neural network approach

  2025 Advanced Modeling and Simulation in Engineering Sciences article Engineering Tribology and Lubrication Engineering Open access

  8

  citations
• Development of a machine learning-based surrogate model for friction prediction in textured journal bearings

  2024 Friction article Engineering Tribology and Lubrication Engineering Open access

  11

  citations
• Machine learning based surrogate modelling for the prediction of maximum contact temperature in EHL line contacts

  2022 Tribology International article Engineering Tribology and Lubrication Engineering

  32

  citations
• Prediction of film thickness in starved EHL point contacts using two-phase flow CFD model

  2022 Tribology International article Engineering Tribology and Lubrication Engineering

  25

  citations

6 results

• Machine learning based anomaly detection and classification of acoustic emission events for wear monitoring in sliding bearing systems

  2020 Tribology International article Engineering Machine Fault Diagnosis Techniques cited by 1 patent

  156

  citations

1 result