There’s a growing argument in the AI space that goes something like this: the models in the cloud aren’t aligned with you, they’re aligned with the company behind them. If you want AI that truly serves your interests, run your own models. Sovereign minds use sovereign AI. I find this argument compelling and incomplete at [...]
Today, I want to share with you another story (after this similar one shared earlier) about bridging machine learning with circuit simulation. Recently, I succeeded in teaching neural networks to speak the language of circuits again, this time however, I solved the spectral bias problem! You can read more about this success in my research [...]
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, [...]
I came across the new Czech Technical University (CTU) in Prague Starting Grant 2026 today, and it looked like one of the more credible early-career principal investigator (PI) “launch your own group” schemes I’ve seen from a European university. The basic offer is strong. The grant funds up to three years, with up to CZK [...]
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, [...]
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 [...]
I just bought a recently published book on Amazon titled Romania after 1989: The Diary of a People in the Shadow of Democracy, and I wanted to share it with you. A few months ago I wrote about Scott Horton and how official narratives can be maintained through repetition, incentives, and selective memory. The same [...]
After I wrote this post earlier this year, today, I want to share with you all another interesting webinar. I know a significant shift when I see one. The new ERC Plus grant is exactly that. I will call it ERC+ below. This isn’t just another funding line; it’s the ERC’s answer to a question from [...]
Every so often, a paper comes along that feels like it bridges two worlds that have been talking past each other. Recently, I came across one of those papers: “Automatic network structure discovery of physics informed neural networks via knowledge distillation” by Ziti Liu and colleagues. It’s an ambitious piece of work that tries to [...]
I recently came across a talk by Emilia Casper, called “Breaking the Wall of Blind Obedience.” I resonate and completely agree with her message, and I encourage everyone to support her work and buy her book „Just Following Orders: Atrocities and the Brain Science of Obedience„. It feels crucial right now, in a time when [...]
Sovereign AI Isn’t EnoughΨ-xLSTM: Automated Behavioral Verilog-A Generation from Distilled Physics-Informed xLSTM Networks for High-Frequency Device ModelingAsPINN and AtPINN: Symmetry and Transfer Learning for Physics-Informed Neural NetworksCzech Technical University in Prague Introduces a New Starting Grant 2026Ψ-HDL: Physics Structure-Informed Neural Networks for Hardware Description Language Generation
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