%0 Generic %1 dehaan2021scaling %A de Haan, Pim %A Rainone, Corrado %A Cheng, Miranda C. N. %A Bondesan, Roberto %D 2021 %K hep-th machine_learning phd qft %T Scaling Up Machine Learning For Quantum Field Theory with Equivariant Continuous Flows %U http://arxiv.org/abs/2110.02673 %X We propose a continuous normalizing flow for sampling from the high-dimensional probability distributions of Quantum Field Theories in Physics. In contrast to the deep architectures used so far for this task, our proposal is based on a shallow design and incorporates the symmetries of the problem. We test our model on the $\phi^4$ theory, showing that it systematically outperforms a realNVP baseline in sampling efficiency, with the difference between the two increasing for larger lattices. On the largest lattice we consider, of size $3232$, we improve a key metric, the effective sample size, from 1% to 66% w.r.t. the realNVP baseline.

@misc{dehaan2021scaling, abstract = {We propose a continuous normalizing flow for sampling from the high-dimensional probability distributions of Quantum Field Theories in Physics. In contrast to the deep architectures used so far for this task, our proposal is based on a shallow design and incorporates the symmetries of the problem. We test our model on the $\phi^4$ theory, showing that it systematically outperforms a realNVP baseline in sampling efficiency, with the difference between the two increasing for larger lattices. On the largest lattice we consider, of size $32\times 32$, we improve a key metric, the effective sample size, from 1% to 66% w.r.t. the realNVP baseline.}, added-at = {2022-12-31T14:40:52.000+0100}, author = {de Haan, Pim and Rainone, Corrado and Cheng, Miranda C. N. and Bondesan, Roberto}, biburl = {https://www.bibsonomy.org/bibtex/254dbc87005d38ce66032b3ae1b77419c/intfxdx}, description = {Scaling Up Machine Learning For Quantum Field Theory with Equivariant Continuous Flows}, interhash = {727db336f3b1a691f099227946d5c864}, intrahash = {54dbc87005d38ce66032b3ae1b77419c}, keywords = {hep-th machine_learning phd qft}, note = {cite arxiv:2110.02673Comment: 8 pages, 5 figures. Fourth Workshop on Machine Learning and the Physical Sciences (NeurIPS 2021)}, timestamp = {2022-12-31T14:40:52.000+0100}, title = {Scaling Up Machine Learning For Quantum Field Theory with Equivariant Continuous Flows}, url = {http://arxiv.org/abs/2110.02673}, year = 2021 }