A new regularized path-dependent McKean-Vlasov formulation with optimization-free tensor density approximation scales enhanced sampling to collective variable dimensions up to 64.
Variational inference and density estimation with non-negative tensor train
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math.NA 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
A two-stage interpolation and second-order fitting procedure compresses high-dimensional discrete probability tensors into non-negative hierarchical Tucker format with O(d) complexity.
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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.
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Variational inference and density estimation with non-negative tensor of hierarchical tucker format
A two-stage interpolation and second-order fitting procedure compresses high-dimensional discrete probability tensors into non-negative hierarchical Tucker format with O(d) complexity.