M-Theory Research: TensorFlow-based code to address some research problems in M-Theory / Superstring Theory / Supergravity / Quantum Gravity. Broadly speaking, M-Theory is all about the very rich mathematical structure that arises if one tries to reconcile the physical principles of Quantum Mechanics, General Relativity, and Supersymmetry. In terms of ”inputs” and ”data”, this research should be regarded as Pure Mathematics, i.e. there is no dependency on ”measurement” (or even ”user”) data. Still, despite this research not using any data examples (for learning or otherwise), Google’s TensorFlow Machine Learning technology is sufficiently generic to be a very useful tool to address some research questions in this domain that can/should be studied numerically.
Keywords for this software
References in zbMATH (referenced in 4 articles )
Showing results 1 to 4 of 4.
- Bobev, Nikolay; Fischbacher, Thomas; Gautason, Fridrik Freyr; Pilch, Krzysztof: A cornucopia of (\mathrmAdS_5) vacua (2020)
- Guarino, Adolfo; Sterckx, Colin; Trigiante, Mario: (\mathcalN= 2) supersymmetric S-folds (2020)
- Comsa, Iulia M.; Firsching, Moritz; Fischbacher, Thomas: SO(8) supergravity and the magic of machine learning (2019)
- Guarino, Adolfo; Sterckx, Colin: S-folds and (non-)supersymmetric Janus solutions (2019)