A Transformer-LSTM-SVR hybrid model for AI-driven emotional optimization in NEV embedded interior systems - Nature
<a href="https://news.google.com/rss/articles/CBMiX0FVX3lxTE90NXYwX0VUWDd1RmpIWGpKQl8wZ2dqNnl4T0lJLWNaNDFzUzk5UnFFV3NOcWtPb1VfaFpmLUxpeGVCQkVhZzl6TkVPWG9jV01kalc5cGxuaEs4cHJUY0tn?oc=5" target="_blank">A Transformer-LSTM-SVR hybrid model for AI-driven emotional optimization in NEV embedded interior systems</a> <font color="#6f6f6f">Nature</font>
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From Physics to Surrogate Intelligence: A Unified Electro-Thermo-Optimization Framework for TSV Networks
arXiv:2603.29268v1 Announce Type: new Abstract: High-density through-substrate vias (TSVs) enable 2.5D/3D heterogeneous integration but introduce significant signal-integrity and thermal-reliability challenges due to electrical coupling, insertion loss, and self-heating. Conventional full-wave finite-element method (FEM) simulations provide high accuracy but become computationally prohibitive for large design-space exploration. This work presents a scalable electro-thermal modeling and optimization framework that combines physics-informed analytical modeling, graph neural network (GNN) surrogates, and full-wave sign-off validation. A multi-conductor analytical model computes broadband S-parameters and effective anisotropic thermal conductivities of TSV arrays, achieving $5\%-10\%$ relative
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