.png){kind=icon}
返回
Self-learning emulators and eigenvector continuation
.png){kind=icon}
OA
AI
DOI：10.1103/PhysRevResearch.4.023214.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Emulators that can bypass computationally expensive scientific calculations with high accuracy and speed can enable new studies of fundamental science as well as more potential applications. In this work we discuss solving a system of constraint equations efficiently using a self-learning emulator. A self-learning emulator is an active learning protocol that can be used with any emulator that faithfully reproduces the exact solution at selected training points. The key ingredient is a fast estimate of the emulator error that becomes progressively more accurate as the emulator is improved, and the accuracy of the error estimate can be corrected using machine learning. We illustrate with three examples. The first uses cubic spline interpolation to find the solution of a transcendental equation with variable coefficients. The second example compares a spline emulator and a reduced basis method emulator to find solutions of a parametrized differential equation. The third example uses eigenvector continuation to find the eigenvectors and eigenvalues of a large Hamiltonian matrix that depends on several control parameters.
期刊
IF:
4.2
论文数:
7.6K
被引数:
2.7W
机构
引用论文
Carbon isotopic composition of fossil leaves from the Early Cretaceous sediments of western India
OAAI
OAAI分享收藏Rigorous constraints on three-nucleon forces in chiral effective field theory from fast and accurate calculations of few-body observables.png)
PHYSICAL REVIEW COAAI
PHYSICAL REVIEW CIF3.4
OAAI分享收藏Incentive Based Multi-Objective Optimization in Electric Vehicle Navigation including Battery Charging含电池充电的电动汽车导航中基于激励的多目标优化OAAI
OAAI分享收藏Eigenvector continuation as an efficient and accurate emulator for uncertainty quantificationPHYSICS LETTERS BOAAI
PHYSICS LETTERS BIF4.5
OAAI分享收藏
查看更多内容