2 citations · 2 across the 4 of their papers we have counts for
4 papers
Convergence for adaptive resampling of random Fourier features
Xin Huang, Aku Kammonen, Anamika Pandey +4
The machine learning random Fourier feature method for data in high dimension is computationally and theoretically attractive since the optimization is based on a convex standard l…
Convergence rates for random feature neural network approximation in molecular dynamics
Xin Huang, Petr Plechac, Mattias Sandberg +1
Random feature neural network approximations of the potential in Hamiltonian systems yield approximations of molecular dynamics correlation observables that have the expected error…
Path integral molecular dynamics approximations of quantum canonical observables
Xin Huang, Petr Plechac, Mattias Sandberg +1
Mean-field molecular dynamics based on path integrals is used to approximate canonical quantum observables for particle systems consisting of nuclei and electrons. A computational…
Canonical mean-field molecular dynamics derived from quantum mechanics
Xin Huang, Petr Plechac, Mattias Sandberg +1
Canonical quantum correlation observables can be approximated by classical molecular dynamics. In the case of low temperature the ab initio molecular dynamics potential energy is b…