A pictorial introduction to differential geometry (2017)
Summary
A pictorial introduction to differential geometry, showing how Maxwell's equations emerge from geometric concepts, based on a 2017 arXiv paper.
View Cached Full Text
Cached at: 05/31/26, 07:32 AM
# A pictorial introduction to differential geometry, leading to Maxwell's equations as three pictures Source: [https://arxiv.org/abs/1709.08492](https://arxiv.org/abs/1709.08492) Bibliographic Tools ## Bibliographic and Citation Tools Bibliographic Explorer Toggle Code, Data, Media ## Code, Data and Media Associated with this Article Demos ## Demos Related Papers ## Recommenders and Search Tools About arXivLabs ## arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website\. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy\. arXiv is committed to these values and only works with partners that adhere to them\. Have an idea for a project that will add value for arXiv's community?[**Learn more about arXivLabs**](https://info.arxiv.org/labs/index.html)\.
Similar Articles
High dimensional geometry is transforming the MRI industry(2017) [pdf]
A 2017 presentation by David Donoho at the AMS discusses how high-dimensional geometry is revolutionizing the MRI industry, likely through compressed sensing and related mathematical techniques.
Steerable Neural ODEs on Homogeneous Spaces
This paper introduces steerable neural ordinary differential equations on homogeneous spaces, providing a geometric framework for learning continuous-time equivariant dynamics.
Riemannian Archetypal Analysis: Interpretable non-linear data analysis on deformed star distributions
This paper introduces a Riemannian version of archetypal analysis using data-driven pullback geometry to combine interpretability with non-linear expressiveness, proposing the Riemannian Archetypal Mapping (RAM) and demonstrating its effectiveness on synthetic data and MNIST.
@antoniolupetti: "Matrix Calculus for Machine Learning and Beyond" is an interesting set of free lecture notes for understanding the mat…
A set of free MIT lecture notes on matrix calculus for machine learning, combining rigorous mathematics with visual explanations.
Fundamental Theorem of Calculus
A personal blog post rigorously introducing the Riemann integral and proving the Fundamental Theorem of Calculus, including supporting theorems like Rolle’s and the Mean Value Theorem.