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Kernel controlled real-time Complex Langevin simulation [PDF]
EPJ Web of Conferences, 2022 This study explores the utility of a kernel in complex Langevin simulations of quantum real-time dynamics on the Schwinger-Keldysh contour. We give several examples where we use a systematic scheme to find kernels that restore correct convergence of ...
Alvestad Daniel +2 more
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Coupling Langevin Dynamics With Continuum Mechanics: Exposing the Role of Sarcomere Stretch Activation Mechanisms to Cardiac Function. [PDF]
Front Physiol, 2018 High-performance computing approaches that combine molecular-scale and macroscale continuum mechanics have long been anticipated in various fields. Such approaches may enrich our understanding of the links between microscale molecular mechanisms and ...
Washio T +4 more
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Accelerating Convergence of Langevin Dynamics via Adaptive Irreversible Perturbations
Mathematics, 2023 Irreversible perturbations in Langevin dynamics have been widely recognized for their role in accelerating convergence in simulations of multi-modal distributions π(θ).
Zhenqing Wu +5 more
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Multi-scale projection operator method and coarse-graining of covariant Fokker-Planck theory
Physical Review Research, 2023 A multi-scale projection operator method is developed and applied to study coarse-graining of covariant Fokker-Planck theory and the associated Ito-Langevin dynamics. Explicit expressions for the renormalized kinetic coefficients are obtained. It is also
Mingnan Ding, Xiangjun Xing
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Learning Langevin dynamics with QCD phase transition [PDF]
EPJ Web of Conferences, 2022 In this proceeding, the deep Convolutional Neural Networks(CNNs) are deployed to recognize the order of QCD phase transition and predict the dynamical parameters in Langevin processes. To overcome the intrinsic randomness existed in a stochastic process,
Wang Lingxiao, Jiang Lijia, Zhou Kai
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Efficient Langevin dynamics for “noisy” forces [PDF]
The Journal of Chemical Physics, 2020 Efficient Boltzmann-sampling using first-principles methods is challenging for extended systems due to the steep scaling of electronic structure methods with the system size. Stochastic approaches provide a gentler system-size dependency at the cost of introducing “noisy” forces, which could limit the efficiency of the sampling.
Eitam Arnon +3 more
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Entropy, 2022 The Jarzynski equality (JE) was originally derived under the deterministic Hamiltonian formalism, and later, it was demonstrated that stochastic Langevin dynamics also lead to the JE.
Javier Varillas, Lamberto Rondoni
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Neural Langevin Dynamical Sampling [PDF]
IEEE Access, 2020 Sampling technique is one of the asymptotically unbiased estimation approaches for inference in Bayesian probabilistic models. Markov chain Monte Carlo (MCMC) is a kind of sampling methods, which is widely used in the inference of complex probabilistic models. However, current MCMC methods can incur high autocorrelation of samples, which means that the
Minghao Gu, Shiliang Sun
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Advances in Condensed Matter Physics, 2022 We report a hybrid quantum-classical simulation approach for simulating the optical phase transition observed experimentally in the ultrahigh-density type-II InAs quantum dot array.
Shiba Kodai +3 more
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Unbiased Estimation Using Underdamped Langevin Dynamics
SIAM Journal on Scientific Computing, 2023 In this work we consider the unbiased estimation of expectations w.r.t.~probability measures that have non-negative Lebesgue density, and which are known point-wise up-to a normalizing constant. We focus upon developing an unbiased method via the underdamped Langevin dynamics, which has proven to be popular of late due to applications in statistics and
Hamza Ruzayqat +2 more
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