Suivre
Matthias Sachs
Titre
Citée par
Citée par
Année
Hyperactive learning for data-driven interatomic potentials
C van der Oord, M Sachs, DP Kovács, C Ortner, G Csányi
npj Computational Materials 9 (1), 168, 2023
602023
Langevin dynamics with variable coefficients and nonconservative forces: from stationary states to numerical methods
M Sachs, B Leimkuhler, V Danos
Entropy 19 (12), 647, 2017
432017
ACEpotentials. jl: A Julia implementation of the atomic cluster expansion
WC Witt, C van der Oord, E Gelžinytė, T Järvinen, A Ross, JP Darby, ...
The Journal of Chemical Physics 159 (16), 2023
292023
Hypocoercivity properties of adaptive Langevin dynamics
B Leimkuhler, M Sachs, G Stoltz
SIAM Journal on Applied Mathematics 80 (3), 1197-1222, 2020
272020
Efficient posterior sampling for high-dimensional imbalanced logistic regression
D Sen, M Sachs, J Lu, DB Dunson
Biometrika 107 (4), 1005-1012, 2020
172020
Ergodic properties of quasi-Markovian generalized Langevin equations with configuration dependent noise and non-conservative force
B Leimkuhler, M Sachs
Stochastic Dynamics Out of Equilibrium: Institut Henri Poincaré, Paris …, 2019
162019
Efficient numerical algorithms for the generalized Langevin equation
B Leimkuhler, M Sachs
SIAM Journal on Scientific Computing 44 (1), A364-A388, 2022
122022
Posterior computation with the Gibbs zig-zag sampler
M Sachs, D Sen, J Lu, D Dunson
Bayesian Analysis 18 (3), 909-927, 2023
102023
Non-reversible Markov chain Monte Carlo for sampling of districting maps
G Herschlag, JC Mattingly, M Sachs, E Wyse
arXiv preprint arXiv:2008.07843, 2020
102020
Quadrature points via heat kernel repulsion
J Lu, M Sachs, S Steinerberger
Constructive Approximation 51 (1), 27-48, 2020
62020
Ergodic properties of quasi-Markovian generalized Langevin equations with configuration dependent noise
B Leimkuhler, M Sachs
arXiv preprint arXiv:1804.04029, 2018
62018
Efficient Numerical Algorithms for the Generalized Langevin Equation. arXiv e-prints, page
B Leimkuhler, M Sachs
arXiv preprint arXiv:2012.04245, 2020
32020
Hyperactive Learning (HAL) for data-driven interatomic potentials
C Oord, M Sachs, DP Kovács, C Ortner, G Csányi
Preprint at https://arxiv. org/abs/2210.04225 10, 2022
22022
Generalised Langevin equation: asymptotic properties and numerical analysis
M Sachs
The University of Edinburgh, 2018
12018
Machine learning configuration-dependent friction tensors in Langevin heatbaths
M Sachs, WG Stark, R Maurer, C Ortner
Machine Learning: Science and Technology, 2025
2025
Equivariant Representation of Configuration-Dependent Friction Tensors in Langevin Heatbaths
M Sachs, WG Stark, RJ Maurer, C Ortner
arXiv preprint arXiv:2407.13935, 2024
2024
Hyperactive learning for data-driven interatomic potentials
O Cvd, M Sachs, D Kovacs, C Ortner, G Csanyi
2022
Le système ne peut pas réaliser cette opération maintenant. Veuillez réessayer plus tard.
Articles 1–17