Chiara Lionello
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chiaralionello.bsky.social
Chiara Lionello
@chiaralionello.bsky.social
Postdoc fellow @ CPC Lab
Department of Applied Science and Technology (DISAT)
Politecnico di Torino, Italy
Reposted by Chiara Lionello
#LEAP is out @pnasnexus.org!🚀
Building on abstract concepts of local fluctuations & their correlations in space & ⏱️, #LEAP provides, in agnostic & purely data-driven way, info on the internal physics of complex dynamical systems from the atomic- to the macro-scale!🤩
academic.oup.com/pnasnexus/ad...
Classification and spatiotemporal correlation of dominant fluctuations in complex dynamical systems
Abstract. The behaviors of many complex systems, from nanostructured materials to animal colonies, are governed by local events/rearrangements that, while
academic.oup.com
February 19, 2025 at 10:03 AM
Reposted by Chiara Lionello
#compchem #chemsky #data-analysis #machinelearning #AI folks 📣

In #ArXiv we propose the concept of “optimal spatiotemporal resolutions”!🤯
We demonstrate how any system/data has its own to be best studied/characterised & how to learn these directly from the system’s data!🚀
arxiv.org/abs/2412.13741
Optimal Spatiotemporal Resolutions
In general, the comprehension of any type of complex system depends on the resolution used to look at the phenomena occurring within it. But identifying a priori, for example, the best time frequencie...
arxiv.org
December 20, 2024 at 8:25 AM
Finally out my last work! If you are dealing with high dimensional data, you should definitely take a look! 😉
When studying complex systems, a common belief is that high-dimensional analyses are desirable to prevent losing important information... but to what extent this is really needed/beneficial remains often unclear.😵‍💫
In our last Arxiv preprint we challenge this assumption:
arxiv.org/abs/2412.094... 🚀🤩
Relevant, hidden, and frustrated information in high-dimensional analyses of complex dynamical systems with internal noise
Extracting from trajectory data meaningful information to understand complex systems might be non-trivial. High-dimensional analyses are typically assumed to be desirable, if not required, to prevent ...
arxiv.org
December 19, 2024 at 2:47 PM