4 papers
Linear-Nonlinear Fusion Neural Operator for Partial Differential Equations
Heng Wu, Junjie Wang, Benzhuo Lu
Neural operator learning directly constructs the mapping relationship from the equation parameter space to the solution space, enabling efficient direct inference in practical appl…
Operator learning on domain boundary through combining fundamental solution-based artificial data and boundary integral techniques
Haochen Wu, Heng Wu, Benzhuo Lu
For linear partial differential equations with known fundamental solutions, this work introduces a novel operator learning framework that relies exclusively on domain boundary data…
Mathematical artificial data for operator learning
Heng Wu, Benzhuo Lu
Machine learning has emerged as a transformative tool for solving differential equations (DEs), yet prevailing methodologies remain constrained by dual limitations: data-driven met…
Machine learning classification of power converter control mode
Rabah Ouali, Jean-Yves Dieulot, Pascal Yim +4
To ensure the proper functioning of the current and future electrical grid, it is necessary for Transmission System Operators (TSOs) to verify that energy providers comply with the…