# Emmy Noether on Energy Conservation in General Relativity

@article{Rowe2019EmmyNO, title={Emmy Noether on Energy Conservation in General Relativity}, author={David E. Rowe}, journal={arXiv: History and Philosophy of Physics}, year={2019} }

During the First World War, the status of energy conservation in general relativity was one of the most hotly debated questions surrounding Einstein's new theory of gravitation. His approach to this aspect of general relativity differed sharply from another set forth by Hilbert, even though the latter conjectured in 1916 that both theories were probably equivalent. Rather than pursue this question himself, Hilbert chose to charge Emmy Noether with the task of probing the mathematical…

## 5 Citations

### Noether's Theorems and Energy in General Relativity

- Physics
- 2021

This paper has three main aims: first, to give a pedagogical introduction to Noether’s two theorems and their implications for energy conservation in general relativity, which was a central point of…

### Substantive general covariance and the Einstein-Klein dispute: A Noetherian approach

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Famously, Klein and Einstein were embroiled in an epistolary dispute over whether General Relativity has any physically meaningful conserved quantities. In this paper, we explore the consequences of…

### General Relativity in the Making, 1916–1918: Rudolf J. Humm in Göttingen and Berlin

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- 2019

In 1916, the Swiss student Rudolf J. Humm arrived in Göttingen to study relativity theory under Hilbert. He enrolled in his courses, attended Klein’s lectures, and interacted with numerous others,…

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The purpose of this article is to consider the metaphysics of geometric and non-geometric objects as they appear in physical theories such as general relativity, and the interactions between these…

### Noether: The More Things Change, the More Stay the Same

- Computer ScienceArXiv
- 2021

A systematic study of the role that symmetry plays, and how symmetry interacts with the learning algorithm is clarified, which can be thought of as one further important tool in understanding the performance of neural networks under gradient descent.

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