As the Ψ-family has grown, a practical question has naturally appeared: with several related frameworks now available, which one should you actually use for a particular scientific or engineering problem? The answer is not simply to choose the newest framework, because these methods are not redundant versions of the same idea. Each one was developed [...]
Attribution and scope: this tutorial builds on Ψ-NN, introduced by Liu et al. (2025, Nature Communications). That foundational framework is not my work. Ψ-HDL, Ψ-xLSTM, and Ψ-Vortex are my own extensions of that research direction. The promise of this tutorial: the companion tutorials left our physics-obeying model able to speak „circuit“ through Ψ-HDL and to [...]
There’s a saying floating around the hardware world that sums up one of the biggest mistakes in Edge AI right now: „Don’t kill a mosquito with a cannon.“ Note: Please, never kill insects. They are advanced living beings and deserve respect. If you hurt one by mistake, apologize for it, as I do. Sometimes this [...]
Happy to announce that I’ve taken a meaningful step toward bridging the gap between AI and circuit/device modeling. It is my absolute pleasure to introduce Ψ-HDL (pronounced Psi-HDL), my Physics Structure-Informed Hardware Description Language framework. This work builds directly on the Ψ-NN (Physics structure-informed neural network) discovery framework introduced by Liu et al. (Nature Communications, [...]
Which 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 Three-Dimensional Neuromorphic DevicesToday I Submitted My First MSCA-PF Research ProposalXiaomi Portable Electric Air Compressor 2 Review: Real-World Test on a Dacia Duster Mat Edition
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