A new regularized path-dependent McKean-Vlasov formulation with optimization-free tensor density approximation scales enhanced sampling to collective variable dimensions up to 64.
Convergence rates for an Adaptive Biasing Potential scheme from a Wasserstein optimization perspective
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Conditions for long-time L^p Wasserstein contraction are derived for non-globally dissipative diffusions, extending to non-elliptic processes with a one-dimensional characterization via the maximal eigenvalue of a Feynman-Kac operator.
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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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Long-time $L^p$ Wasserstein contraction for diffusion processes without global dissipativity
Conditions for long-time L^p Wasserstein contraction are derived for non-globally dissipative diffusions, extending to non-elliptic processes with a one-dimensional characterization via the maximal eigenvalue of a Feynman-Kac operator.