Tools for exploiting Morphological Symmetries in robotics

On-the-fly generator of space-group irreducible representations

Isomorphisms of quiver representations applied to neural networks.

many powerful tools for studying irreducible representations of SU(n), including making animations of hadron flavor-state multiplets

AIDN is a deep learning algorithm to represent any finitely-presented algebraic object with a set of deep neural networks.

Computes representation matrices for Lie groups

Drafts of textbooks. One on the math inspired by supersymmetric field theories. One on low dimensional (below 3+1) integrable physics.

Search for non-trivial elements of the kernel of the Burau representation of the four-strand braid group.

A Python-based Rubik's Cube simulator and visualizer using matrix algebra for efficient representation, manipulation, and visualization of the cube's state. Includes a group theory approach to cube operations and a proof-of-concept visualization module using Matplotlib.

Compute the knowledge matrices associated to a neural network.

Very simple version of the code used for the experiments in the paper Hidden Activations Are Not Enough: A General Approach to Neural Networks Predictions.

Linear-time sequence modeling that replaces attention's O(n²d) complexity with O(nd) summation-based aggregation. Demonstrates constraint-driven emergence: how functional representations can develop from optimization pressure and architectural constraints alone, without explicit pairwise interactions.