Gradient Networks for Universal Magnetic Modeling of Synchronous Machines

arXiv:2602.14947 · eess.SY, cs.LG, cs.SY · Submitted 2026-02-16 · Read on arXiv

eess.SY, cs.LG, cs.SY

Submitted: 2026-02-16

Updated: 2026-09-24

Code: https://github.com/Aalto-Electric-Drives/motulator

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Accuracy — 9× in this paper · explained in 1 episode
• Activation Function — 8× in this paper · explained in 1 episode
• Consistency — 3× in this paper · explained in 3 episodes
• Core — 3× in this paper · explained in 2 episodes
• Dynamic Modeling — 3× in this paper · explained in 3 episodes
• Fidelity — 3× in this paper · explained in 2 episodes
• Generalization — 3× in this paper · explained in 13 episodes
• Closed-Loop Control — 2× in this paper · explained in 2 episodes
• Physical Constraints — 2× in this paper · explained in 7 episodes
• AdamW Optimizer — 1× in this paper · explained in 1 episode
• Inverse Problem — 1× in this paper · explained in 2 episodes
• Loss Function — 1× in this paper · explained in 2 episodes
• Robustness — 1× in this paper · explained in 5 episodes

Sources

• Lagrangian Neural Networks
• Hamiltonian Neural Networks
• Squareplus: A Softplus-Like Algebraic Rectifier
• Decoupled Online Feedforward Generation of Optimal References for Saturated Synchronous Machine Drives

Related papers

• One Request, Multiple Experts: LLM Orchestrates Domain Specific Models via Adaptive Task Routing
• A Geometric Decision Procedure for STL Feasibility and Repair
• From Asymptotic to Designer-Assigned-Time Control: A Review of Stability Notions, Design Mechanisms, and Controller Architectures
• Battery Second Life: A Review of Experimental Studies
• Submodular Multi-Agent Policy Learning for Online Distributed Task Allocation in Open Multi-Agent Systems
• Policy-Level Recursive Self-Improvement for Embodied AI with a Criticality World Model