Fine, regular, and star triangulations (FRSTs) of four-dimensional reflexive polytopes give rise to toric varieties, within which generic anticanonical hypersurfaces yield smooth Calabi-Yau threefolds. We introduce CYTransformer, a deep learning model based on the transformer architecture, to automate the generation of FRSTs. We demonstrate that CYTransformer efficiently and unbiasedly samples...
We introduce a neural network-based method to bootstrap crossing equations in Conformal Field Theory at finite temperature. Traditional approaches relying on positivity constraints or truncation schemes that discard infinite towers of operators are not applicable to this problem. Instead, we use MLPs to model spin-dependent tail functions that capture the combined contribution of infinitely...
The past few years have seen major advances in understanding the properties of axions in string theory. This progress is thanks to new computational tools that allow for fast and automated calculations with Calabi-Yau manifolds. I will describe the predictions string theory makes for axion masses, decay constants, and axion-photon couplings, and how these depend precisely on the topology of...
A numerical scheme based on semi-supervised machine learning, "AInstein", was recently introduced (see https://iopscience.iop.org/article/10.1088/3050-287X/ae1117) to approximate generic Riemannian Einstein metrics on a given manifold. Its versatility stems from encoding the differentiable structure directly in the loss function, making the method applicable to manifolds constructed in a...
We address the inverse problem in Type IIB flux compactifications of identifying flux vacua with targeted phenomenological properties such as specific superpotential values or tadpole constraints using conditional generative models. These machine learning techniques overcome computational bottlenecks in traditional approaches such as rejection sampling and Markov Chain Monte Carlo (MCMC),...
Special Lagrangian (sLags) submanifolds are crucial objects for string phenomenology and the SYZ conjecture, yet their explicit construction remains a significant challenge in geometry. In this talk, we introduce a novel computational framework to tackle this problem. Our approach leverages a Quality-Diversity (QD) search algorithm to navigate families of parametrized geometries. This method...
We discus the application of two types of generative models are applied to the construction of ISD flux vacua of type IIB string theory. The models under study are Bayesian Flow Networks (BFNs) and Transformers. We find that both models demonstrate good performance, in particular on interpolation and conditional sampling. Some extrapolation capabilities are observed, which could be leveraged...
String theory naturally gives rise to vast, high-dimensional datasets, yet systematic investigations of this landscape has long been impeded by the complexity of moduli spaces, quantum corrections, and the vastness of flux configurations. In this talk, I present new differentiable frameworks for string compactifications that combine automatic differentiation, and ML-based inference to...
The Z-hat invariant defined by Gukov–Pei–Putrov–Vafa is a BPS-counting q-series built from Calabi–Yau geometry, whose values at roots of unity recover the Witten–Reshetikhin–Turaev invariants of 3-manifolds. In this talk, I will review a construction for its knot-theoretic counterpart, the Gukov–Manolescu series F_K, and present the first large-scale computation of F_K for 1,246 knots,...
From holography, we know that gravitational systems have codimension one degrees of freedom. Through a number of experiments, we use machine learning to study physical and mathematical aspects of black hole entropy.
I will present a new bootstrap method for conformal field theories at finite temperature that uses neural networks to capture infinite operator contributions without positivity constraints or truncations. The approach combines the KMS condition, thermal dispersion relations, and neural networks that learn the high-spin behaviour of the thermal block expansion. I will demonstrate the method on...
We describe two related developments at the intersection of string theory, holography, and scientific machine learning. First, we develop a flexible physics-informed neural network (PINN) framework to solve boundary value problems for minimal surfaces in curved spacetimes, with a particular emphasis on handling singular geometries and moving boundaries. The method encodes the governing...
