Scale Dynamics: Beyond the Linear Approximation
Exploring recent advances in scale dynamics: canonical transformations, irreversible flows, and non-linear momentum corrections to renormalization group equations.
Read More →About Me
I am a Ph.D. student at the Edmond and Lily Safra Center for Brain Sciences (ELSC) at The Hebrew University of Jerusalem. My research focuses on scales of description in science and physics, exploring how different levels of abstraction relate to one another. Using machine learning tools, particularly artificial neural networks (ANNs), I study how high-dimensional information can be transformed into meaningful lower-dimensional representations.
Publications
Two pathways to resolve relational inconsistencies
We studied how theories change in response to observations that contradict them. Our ANN's choice of resolution depended on the inconsistency's magnitude, highlighting flexibility in forming descriptions.
Untrained neural networks can demonstrate memorization-independent abstract reasoning
We tested an ANN's ability to extract a predictably changing feature from image sequences with various changing features. The ANN performed well on intelligence tests requiring this ability, validating its use in modeling the extraction of abstract theories.
Recent Blog Posts
Explore my latest thoughts on neural networks, cognitive science, and scales of description.
From time to scale: A gentle introduction to scale dynamics
A proposal for treating the Hamiltonian itself as a dynamical variable that evolves with scale, offering a unified framework for understanding renormalization group flows and cross-scale phenomena.
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