8 citations · 18 across the 7 of their papers we have counts for
9 papers
Data Free Training of Diffusion Models for Lattice Field Theories
Dennis Grinwald, Ankur Singha, Khaled Kahouli +2
Sampling equilibrium field configurations remains a major computational bottleneck in lattice field theory, motivating the development of generative samplers that can produce indep…
Multilevel Plaquette-Space Sampling for Lattice Gauge Theories with Local Constraint Solves
Ankur Singha, Jacob Finkenrath, Karl Jansen +2
Lattice gauge theories are an important class of physical models with high-dimensional structured distributions that underpin first-principles calculations in particle, nuclear, an…
Sector-Resolved Flow Sampling for Topologically Frozen Lattice Gauge Theories
Ankur Singha, Jacob Kauffmann, Karl Jansen +3
Topological fluctuations are essential to nonperturbative gauge theories but become increasingly difficult to sample toward the continuum limit, where Markov chains can freeze in s…
Scalable Generative Sampling and Multilevel Estimation for Lattice Field Theories Near Criticality
A. Singha, J. Kauffmann, E. Cellini +2
Sampling lattice field theories near criticality is severely hindered by critical slowing down, which makes standard Markov chain methods increasingly inefficient at large lattice…
Multilevel Generative Samplers for Investigating Critical Phenomena
Ankur Singha, Elia Cellini, Kim A. Nicoli +3
Investigating critical phenomena or phase transitions is of high interest in physics and chemistry, for which Monte Carlo (MC) simulations, a crucial tool for numerically analyzing…
Improvement of Heatbath Algorithm in LFT using Generative models
Ali Faraz, Ankur Singha, Dipankar Chakrabarti +2
The Heatbath Algorithm is commonly used for sampling in local lattice field theories, but performing exact updates or sampling from the local density is challenging when dealing wi…
Sampling U(1) gauge theory using a re-trainable conditional flow-based model
Ankur Singha, Dipankar Chakrabarti, Vipul Arora
Sampling topological quantities in the Monte Carlo simulation of Lattice Gauge Theory becomes challenging as we approach the continuum limit of the theory. In this work, we introdu…
Conditional Normalizing flow for Monte Carlo sampling in lattice scalar field theory
Ankur Singha, Dipankar Chakrabarti, Vipul Arora
The cost of Monte Carlo sampling of lattice configurations is very high in the critical region of lattice field theory due to the high correlation between the samples. This paper s…
Generative learning for the problem of critical slowing down in lattice Gross Neveu model
Ankur Singha, Dipankar Chakrabarti, Vipul Arora
In lattice field theory, Monte Carlo simulation algorithms get highly affected by critical slowing down in the critical region, where autocorrelation time increases rapidly. Hence…