mlphysics

Table of Contents

1. Machine Learning & Physics

1.1. Geometric Foundations of ML

1.1.5. Manifold hypothesis - Wikipedia

The manifold hypothesis posits that many high-dimensional data sets that occur in the real world actually lie along low-dimensional latent manifolds inside that high-dimensional space.
If your space is non linear, maybe it’s linear in some high-dimensional abstract vector space nonsense

1.1.16. [2210.02671] A Logic for Expressing Log-Precision Transformers Transformers as first-order logic

Transformer models can be re-expressed in first-order logic.

1.5. ML to predict physical equations / dynamics

1.5.2. Automated discovery of fundamental variables hidden in experimental data | Nature Computational Science

1.5.3. Learning to Simulate Complex Physics with Graph Networks | DeepMind

Our framework—which we term “Graph Network-based Simulators” (GNS)—represents the state of a physical system with particles, expressed as nodes in a graph, and computes dynamics via learned message-passing

1.6. Renormalization group & Neural networks

1.7. Links

1.9. Thousand Brains Project | Numenta How the Brain Works + AI

1.11. Generative Adversarial Networks (GANs)

1.13.

1.17. HD/VSA Hyperdimensional Computing / Vector Symbolic Architectures

1.18. Ising formulations of many NP problems

1.19. Bayesian Statistics & Neural Networks   science someday_20230330

1.19.3. Ver “Statistical Rethinking 2022” en YouTube

1.21. Themesis - Alianna J. Maren - Physics + Neural Networks

Author: Julian Lopez Carballal

Created: 2025-07-24 Thu 05:25