skip to main content


Search for: All records

Award ID contains: 1847770

Note: When clicking on a Digital Object Identifier (DOI) number, you will be taken to an external site maintained by the publisher. Some full text articles may not yet be available without a charge during the embargo (administrative interval).
What is a DOI Number?

Some links on this page may take you to non-federal websites. Their policies may differ from this site.

  1. Free, publicly-accessible full text available March 31, 2025
  2. Mengesha, Tadele ; Salgado, Abner J (Ed.)
    Inspired by the blending method developed by [P. Seleson, S. Beneddine, and S. Prudhome, A Force-Based Coupling Scheme for Peridynamics and Classical Elasticity, (2013)] for the nonlocal-to-local coupling, we create a symmetric and consistent blended force-based atomistic-to-continuum (a/c) scheme for the atomistic chain in one-dimensional space. The conditions for the well-posedness of the underlying model are established by analyzing an optimal blending size and blending type to ensure the H1{\$}{\$}H^1{\$}{\$}semi-norm stability for the blended force-based operator. We present several numerical experiments to test and confirm the theoretical findings. 
    more » « less
  3. Mengesha, Tadele ; Salgado, Abner J (Ed.)
  4. Efficient simulation of SDEs is essential in many applications, particularly for ergodic systems that demand efficient simulation of both short-time dynamics and large-time statistics. However, locally Lipschitz SDEs often require special treatments such as implicit schemes with small time-steps to accurately simulate the ergodic measures. We introduce a framework to construct inference-based schemes adaptive to large time-steps (ISALT) from data, achieving a reduction in time by several orders of magnitudes. The key is the statistical learning of an approximation to the infinite-dimensional discrete-time flow map. We explore the use of numerical schemes (such as the Euler-Maruyama, the hybrid RK4, and an implicit scheme) to derive informed basis functions, leading to a parameter inference problem. We introduce a scalable algorithm to estimate the parameters by least squares, and we prove the convergence of the estimators as data size increases.

    We test the ISALT on three non-globally Lipschitz SDEs: the 1D double-well potential, a 2D multiscale gradient system, and the 3D stochastic Lorenz equation with a degenerate noise. Numerical results show that ISALT can tolerate time-step magnitudes larger than plain numerical schemes. It reaches optimal accuracy in reproducing the invariant measure when the time-step is medium-large.

     
    more » « less