Zobrazeno 1 - 10
of 185
pro vyhledávání: '"Ruehle, Fabian"'
Autor:
Ruehle, Fabian, Sung, Benjamin
We connect recent conjectures and observations pertaining to geodesics, attractor flows, Laplacian eigenvalues and the geometry of moduli spaces by using that attractor flows are geodesics. For toroidal compactifications, attractor points are related
Externí odkaz:
http://arxiv.org/abs/2408.00830
Deformations of the heterotic superpotential give rise to a topological holomorphic theory with similarities to both Kodaira-Spencer gravity and holomorphic Chern-Simons theory. Although the action is cubic, it is only quadratic in the complex struct
Externí odkaz:
http://arxiv.org/abs/2406.04393
We explore the symmetry structure of Type II Little String Theories and their T-dualities. We construct these theories both from the bottom-up perspective starting with seed Superconformal Field Theories, and from the top-down using F-/M-theory. By e
Externí odkaz:
http://arxiv.org/abs/2405.03877
Autor:
Liu, Ziming, Wang, Yixuan, Vaidya, Sachin, Ruehle, Fabian, Halverson, James, Soljačić, Marin, Hou, Thomas Y., Tegmark, Max
Inspired by the Kolmogorov-Arnold representation theorem, we propose Kolmogorov-Arnold Networks (KANs) as promising alternatives to Multi-Layer Perceptrons (MLPs). While MLPs have fixed activation functions on nodes ("neurons"), KANs have learnable a
Externí odkaz:
http://arxiv.org/abs/2404.19756
We study the duality between the Spin$(32)/\mathbb{Z}_2$ heterotic string without vector structure and F-theory with frozen singularities. We give a complete description in theories with $6$d $\mathcal{N}=(1,0)$ supersymmetry and identify the duals o
Externí odkaz:
http://arxiv.org/abs/2404.02191
Autor:
Andriot, David, Ruehle, Fabian
Finding string backgrounds with de Sitter spacetime, where all approximations and corrections are controlled, is an open problem. We revisit the search for de Sitter solutions in the classical regime for specific type IIB supergravity compactificatio
Externí odkaz:
http://arxiv.org/abs/2403.07065
Machine learning techniques are increasingly powerful, leading to many breakthroughs in the natural sciences, but they are often stochastic, error-prone, and blackbox. How, then, should they be utilized in fields such as theoretical physics and pure
Externí odkaz:
http://arxiv.org/abs/2402.13321
We explore the T-duality web of 6D Heterotic Little String Theories, focusing on flavor algebra reducing deformations. A careful analysis of the full flavor algebra, including Abelian factors, shows that the flavor rank is preserved under T-duality.
Externí odkaz:
http://arxiv.org/abs/2311.02168
Autor:
Halverson, James, Ruehle, Fabian
We develop a theory of flows in the space of Riemannian metrics induced by neural network gradient descent. This is motivated in part by recent advances in approximating Calabi-Yau metrics with neural networks and is enabled by recent advances in und
Externí odkaz:
http://arxiv.org/abs/2310.19870
We apply Bayesian optimization and reinforcement learning to a problem in topology: the question of when a knot bounds a ribbon disk. This question is relevant in an approach to disproving the four-dimensional smooth Poincar\'e conjecture; using our
Externí odkaz:
http://arxiv.org/abs/2304.09304