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Tsinghua University researchers propose a Bounded Dual-Loop Automated Schematic Synthesis Framework that converts natural language specifications into verified KiCad schematics for power electronics, boosting component verification coverage from 22.2% to 98.6% and SPICE pass rates from 0% to 100% versus a vanilla LLM baseline.
CAHR-Net proposes a condition-adaptive hysteresis reconstruction network that improves magnetic core loss modeling by injecting operating conditions into intermediate representations, achieving lower errors with fewer parameters compared to existing methods.
This preprint proposes PI-HNO, a physics-informed hybrid neural operator for transient magnetization prediction in power magnetics, achieving low B-H energy consistency errors with only 4,777 trainable parameters per material model.
This paper proposes a Neural Controlled Differential Equation (Neural CDE) framework for learning continuous-time surrogate models of grid-forming inverters for EMT simulation, incorporating dual slow/fast pathways and physics-inspired regularization to capture multi-time-scale dynamics.