4 papers
Physics-informed Machine Learning for Static Friction Modeling in Robotic Manipulators Based on Kolmogorov-Arnold Networks
Yizheng Wang, Timon Rabczuk, Yinghua Liu
Friction modeling plays a crucial role in achieving high-precision motion control in robotic operating systems. Traditional static friction models (such as the Stribeck model) are…
Multi-Head Neural Operator for Modelling Interfacial Dynamics
Mohammad Sadegh Eshaghi, Navid Valizadeh, Cosmin Anitescu +3
Interfacial dynamics underlie a wide range of phenomena, including phase transitions, microstructure coarsening, pattern formation, and thin-film growth, and are typically describe…
Transfer Learning in Physics-Informed Neural Networks: Full Fine-Tuning, Lightweight Fine-Tuning, and Low-Rank Adaptation
Yizheng Wang, Jinshuai Bai, Mohammad Sadegh Eshaghi +4
AI for PDEs has garnered significant attention, particularly Physics-Informed Neural Networks (PINNs). However, PINNs are typically limited to solving specific problems, and any ch…
Variational Physics-informed Neural Operator (VINO) for Solving Partial Differential Equations
Mohammad Sadegh Eshaghi, Cosmin Anitescu, Manish Thombre +3
Solving partial differential equations (PDEs) is a required step in the simulation of natural and engineering systems. The associated computational costs significantly increase whe…