Uncertainty in AI driven Monte Carlo simulations

Analysis of the Ill-Conditioning of the Discrete Inverse Laplace transform in Monte Carlo Simulations of Quantum Many-Body Systems
Phil-Alexander Hofmann, Thomas Chuna, Tobias Dornheim +1
arXiv7 Sep 2026PreprintarXiv licenceFull textNumerical analysis & scientific computingarXiv
Fractional reversible chemical kinetics: Time-Driven Monte Carlo method and application to enzyme conformational dynamics
Nelson H. T. Lemes, Higor V. M. Ferreira
arXiv24 Oct 2026PreprintarXiv licenceFull textMathematical physicsarXiv
Hamiltonian Monte Carlo vs. event-chain Monte Carlo: An appraisal of sampling strategies beyond the diffusive regime
Werner Krauth
Journal of Computational Physics8 Oct 2026Peer-reviewedLicence not stated10 citedStatisticsOpenAlex
Improved Guarantees for Langevin Monte Carlo with Average Smoothness
Arnak S. Dalalyan, Avetik Karagulyan
HAL29 May 2026PreprintLicence not statedGeneral mathematicsHAL
An Efficient Monte Carlo Method for Valid Prior-Free Possibilistic Statistical Inference
Ryan Martin
Journal of the American Statistical Association13 May 2026Peer-reviewedLicence not statedFull text12 citedStatisticsOpenAlex

Cite

BibTeX

@article{tzivrailis2026uncertainty,
  title = {{Uncertainty in AI driven Monte Carlo simulations}},
  author = {Dimitrios Tzivrailis and Alberto Rosso and Eiji Kawasaki},
  journal = {Physical Review E},
  year = {2026},
  month = sep,
  doi = {10.1103/6dxn-p9nt},
  eprint = {2506.14594},
  archivePrefix = {arXiv},
  url = {https://hal.science/hal-05703151v1},
}

RIS

TY  - JOUR
TI  - Uncertainty in AI driven Monte Carlo simulations
AU  - Dimitrios Tzivrailis
AU  - Alberto Rosso
AU  - Eiji Kawasaki
PY  - 2026
DA  - 2026/09/10
JO  - Physical Review E
DO  - 10.1103/6dxn-p9nt
UR  - https://hal.science/hal-05703151v1
SN  - 2470-0045
SN  - 2470-0053
ER  -

Related

Cite