Search Results for author:
Found 4 papers, 0 papers with code
Physics-Informed Induction Machine Modelling
This rapid communication devises a Neural Induction Machine (NeuIM) model, which pilots the use of physics-informed machine learning to enable AI-based electromagnetic transient simulations.
Scalable Neural Dynamic Equivalence for Power Systems
Traditional grid analytics are model-based, relying strongly on accurate models of power systems, especially the dynamic models of generators, controllers, loads and other dynamic components.
Physics-Aware Neural Dynamic Equivalence of Power Systems
The contributions are threefold: (1) an ODE-Net-enabled NeuDyE formulation to enable a continuous-time, data-driven dynamic equivalence of power systems; (2) a physics-informed NeuDyE learning method (PI-NeuDyE) to actively control the closed-loop accuracy of NeuDyE without an additional verification module; (3) a physics-guided NeuDyE (PG-NeuDyE) to enhance the method's applicability even in the absence of analytical physics models.
Data augmentation in microscopic images for material data mining
Recent progress in material data mining has been driven by high-capacity models trained on large datasets.
