In physical systems, transport takes many forms, such as electric current through a wire, heat through metal, or even water through a pipe. Each of these flows can be described by how easily the underlying quantity—charge, energy, or mass—moves through a material. Normally, collisions and friction lead to resistance causing these flows to slow down or fade away. But in a new experiment at TU Wien, scientists have observed a system where that doesn’t happen at all.

By showing that the electronic topology of a material can be tuned by adding or removing electrons, the study opens new possibilities for seamlessly integrating emerging quantum materials technology with established electronics.

New mathematical tools shed light on the fluctuations of living matter

Fluctuations in such energy-consuming systems cannot be assessed by traditional physics due to the influence of the arrow of time on their behavior

Quantitative predictions on the behavior of active matter can facilitate the experimental design of such systems

In this study, we proposed a novel Knowledge-Informed Deep Learning (KIDL) paradigm that, to the best of our knowledge, is the first to unify behavioral generalization and traffic flow stability by systematically integrating high-level knowledge distillation from LLMs with physically grounded stability constraints in car-following modeling. Generalization is enhanced by distilling car-following knowledge from LLMs into a lightweight and efficient neural network, while local and string stability are achieved by embedding physically grounded constraints into the distillation process. Experimental results on real-world traffic datasets validate the effectiveness of the KIDL paradigm, showing its ability to replicate and even surpass the LLM's generalization performance. It also outperforms traditional physics-based, data-driven, and hybrid CFMs by at least 10.18% in terms of trajectory simulation error RMSE. Furthermore, the resulting KIDL model is proven through theoretical and numerical analysis to ensure local and string stability at all equilibrium states, offering a strong foundation for advancing AV technologies.

Practically, KIDL offers a deployable solution for AV control, serving as a high-level motion reference that ensures realistic and stable car-following in mixed traffic environments. Moreover, this framework provides a promising pathway for integrating LLM-derived knowledge into traffic modeling by distilling it into a lightweight model with embedded physical constraints, balancing generalization with real-world feasibility.