8 citations · 18 across the 8 of their papers we have counts for
8 papers
Surface Water Wave Scattering and the Hydrotope
Nima Arkani-Hamed, Francesco Calisto, Nail Ussembayev +2
We study the classical tree-level scattering amplitudes of deep-water surface gravity waves using the methods of high-energy physics. For scattering in one horizontal dimension and…
An Ultraviolet Finite Theory of Scalars
Francesco Calisto, Clifford Cheung
We construct a theory of scalars that is free of short-distance infinities to all orders in perturbation theory. Loop divergences are neutralized by momentum-dependent interactions…
The Equivalence Principle at High Energies Completes the Spectrum
Francesco Calisto, Clifford Cheung, Grant N. Remmen +2
We prove a version of the completeness hypothesis that follows from the coexistence of symmetry and gravity: tree-level gravitational scattering mandates single-particle states in…
Inverse Problem in Effective Field Theory
Francesco Calisto, Clifford Cheung, Grant N. Remmen +2
We show that the tree-level spectrum of heavy particles can be directly extracted from the Wilson coefficients of the corresponding effective field theory at low energies. This pro…
Completeness from Gravitational Scattering
Francesco Calisto, Clifford Cheung, Grant N. Remmen +2
We prove that symmetry in the presence of gravity implies a version of the completeness hypothesis. For a broad class of theories, we demonstrate that the existence of finitely man…
Learning Feynman integrals from differential equations with neural networks
Francesco Calisto, Ryan Moodie, Simone Zoia
We perform an exploratory study of a new approach for evaluating Feynman integrals numerically. We apply the recently-proposed framework of physics-informed deep learning to train…
NeuralPDE: Automating Physics-Informed Neural Networks (PINNs) with Error Approximations
Kirill Zubov, Zoe McCarthy, Yingbo Ma +11
Physics-informed neural networks (PINNs) are an increasingly powerful way to solve partial differential equations, generate digital twins, and create neural surrogates of physical…
Physics-Informed Machine Learning Simulator for Wildfire Propagation
Luca Bottero, Francesco Calisto, Giovanni Graziano +4
The aim of this work is to evaluate the feasibility of re-implementing some key parts of the widely used Weather Research and Forecasting WRF-SFIRE simulator by replacing its core…