Active learning for saddle point calculation
From MaRDI portal
Publication:2103465
DOI10.1007/s10915-022-02040-1zbMath1503.62066arXiv2108.04698OpenAlexW4308532668MaRDI QIDQ2103465
Publication date: 13 December 2022
Published in: Journal of Scientific Computing (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/2108.04698
Nonparametric regression and quantile regression (62G08) Optimal statistical designs (62K05) Algorithms for approximation of functions (65D15) Sequential statistical design (62L05)
Related Items (1)
Uses Software
Cites Work
- Unnamed Item
- Unnamed Item
- Iterative minimization algorithm for efficient calculations of transition states
- Multiscale gentlest ascent dynamics for saddle point in effective dynamics of slow-fast system
- Global optimization-based dimer method for finding saddle points
- Gaussian process surrogates for failure detection: a Bayesian experimental design approach
- Optimization-based Shrinking Dimer Method for Finding Transition States
- Active Learning
- The gentlest ascent dynamics
- Multivariate stochastic approximation using a simultaneous perturbation gradient approximation
- Simplified gentlest ascent dynamics for saddle points in non-gradient systems
- EXPLICIT ESTIMATION OF DERIVATIVES FROM DATA AND DIFFERENTIAL EQUATIONS BY GAUSSIAN PROCESS REGRESSION
- Advanced Lectures on Machine Learning
- Elements of Information Theory
- An Iterative Minimization Formulation for Saddle Point Search
This page was built for publication: Active learning for saddle point calculation