A new regularized path-dependent McKean-Vlasov formulation with optimization-free tensor density approximation scales enhanced sampling to collective variable dimensions up to 64.
Tensor-traindensityestimation,in:deCampos,C.,Maathuis,M.H.(Eds.),Proceedingsof the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, PMLR
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High-Dimensional Enhanced Sampling via Regularized Path-Dependent McKean--Vlasov Dynamics using Tensor Density Approximation
A new regularized path-dependent McKean-Vlasov formulation with optimization-free tensor density approximation scales enhanced sampling to collective variable dimensions up to 64.