Today, my latest work, “Ψ-Vortex: Structure-Regularized Recurrent Learning for Latent Thermal-Coupling Inference and Verilog-A Compact Modeling of 3-D Neuromorphic Devices,” has been published in IEEE Access (IF: 4.2). The supplementary experiments document can be seen here. The Ψ-Vortex design rationale document can be seen here. The code, data, configurations, trained artifacts, and reproducibility materials for [...]
After I published my earlier blog post on Ψ-NN (Physics Structure-Informed Neural Networks), I received a very kind comment from one of the authors. In that reply, they pointed me to two earlier projects from the same research line: AsPINN (Adaptive Symmetry-Recomposition Physics-Informed Neural Networks) and AtPINN (Adaptive Transfer Learning for PINN). At first glance, [...]
My research paper „A Physics-Regularized Neural Surrogate Framework for Printed Memristors“ was published today in the IEEE Access journal (IF: 3.6). I had the idea for this paper initially while working on the „Print the Brain“ project at TU Chemnitz in 2024, and thought about it in my free time while at ERCEA in Brussels [...]
Physics Structure-Informed Modeling for Emerging ElectronicsWhich Ψ Framework Should You Use? Ψ-NN, Ψ-HDL, Ψ-xLSTM, or Ψ-Vortex?A from-scratch tutorial on Ψ-Vortex („Psi-Vortex“)Ψ-Vortex: Structure-Regularized Recurrent Learning for Latent Thermal-Coupling Inference and Verilog-A Compact Modeling of 3-D Neuromorphic DevicesToday I Submitted My First MSCA-PF Research Proposal
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