7 papers
A user's guide to PINNs in geometric analysis: lessons from the asymptotic Plateau problem
Tancredi Schettini Gherardini
This proceedings contribution elaborates on the findings of arXiv:2605.26234v2: a joint work with Marco Usula, where we introduced a machine learning framework based on physics-inf…
Black Hole Black Boxes: Numerical Black Hole Metrics via AInstein Neural Networks
Tancredi Schettini Gherardini, Edward Hirst, Alexander George Stapleton
The AInstein architecture introduced an unsupervised neural method for solving the Riemannian Einstein equations on arbitrary manifolds. This Physics Informed Neural Network approa…
Minimal surfaces, Knots, and Neural Networks
Tancredi Schettini Gherardini, Marco Usula
A recent conjecture by Joel Fine posits a relationship between the coefficients of the HOMFLY polynomial of a knot in the 3-sphere , and the signed count of minimal surfac…
A Physicist's Visit to Exotic Spheres
Tancredi Schettini Gherardini
This thesis discusses exotic 7-spheres, i.e. manifolds that are homeomorphic but not diffeomorphic to the ordinary 7-sphere, using a set of analytical and computational tools from…
A Machine Learning Approach to the Nirenberg Problem
Gianfranco Cortés, Maria Esteban-Casadevall, Yueqing Feng +4
This work introduces the Nirenberg Neural Network: a numerical approach to the Nirenberg problem of prescribing Gaussian curvature on for metrics that are pointwise conformal…
Machine Learning Toric Duality in Brane Tilings
Pietro Capuozzo, Tancredi Schettini Gherardini, Benjamin Suzzoni
We apply a variety of machine learning methods to the study of Seiberg duality within 4d quantum field theories arising on the worldvolumes of D3-branes probing tor…