4 papers
Learning holographic QCD with unflavored meson spectra
Mathew Thomas Arun, Ritik Pal
We develop a data-driven neural network framework to reconstruct the five-dimensional background geometry, the dilaton potential, and the chiral-symmetry-breaking scalar potential…
RG evolution and effect of intermediate new-physics on four-fermion operators
Mathew Thomas Arun, Shyam M, Ritik Pal
Motivated by the stringent experimental bounds on proton lifetime and the need for precise low-energy predictions, there has been renewed interest in the renormalization group (RG)…
Solving Navier-Stokes Equations Using Data-free Physics-Informed Neural Networks With Hard Boundary Conditions
Ritik Pal, Soubhik Mukherjee, Urmi Dutta +1
In recent years, Physics-Informed Neural Networks (PINNs) have emerged as a powerful and robust framework for solving nonlinear differential equations across a wide range of scient…
RG evolution and effect of intermediate new physics on six-quark operators
Mathew Thomas Arun, Shyam M, Ritik Pal
The recent identification of possible 11 neutron-antineutron (-) oscillation candidate events at Super-Kamiokande has renewed the interest in transitions. In…