Synapse
⌘+K
Synapse
PulseExploreClubsResearchersJournals
Instagram
HomeClubsExplore
September 10, 2025Nature Computational ScienceOpen Access

Deep active optimization for complex systems

View Full Paper
Ask AI
Bookmark
Share

Authors

YWYe WeiBPBo PengRXRuiwen Xie

Discussion

Loading...

Member takes

Overview

New optimization methods find better solutions in complex systems with limited data, suggesting advancements in artificial intelligence applications.

Key Points

  • The method identifies optimal solutions in problems with up to 2,000 dimensions, far exceeding current limitations.
  • It outperforms existing algorithms which are limited to 100 dimensions, showing a distinct advantage in efficiency.
  • This approach uses a deep neural surrogate to iteratively optimize solutions, while minimizing the required data samples.
  • The findings highlight significant implications for knowledge discovery across various quantitative fields beyond just scientific applications.

Cite This Study

Wei et al. (2025) studied this question.

synapsesocial.com/papers/68c1d03e54b1d3bfb60f71c2https://doi.org/10.1038/s43588-025-00858-x
View Full Paper
Ask AI
Bookmark
Share

Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1COMPUTATIONAL INTELLIGENCE AND MATHEMATICAL APPLICATIONS2026
  2. 2COMPUTATIONAL INTELLIGENCE AND MATHEMATICAL APPLICATIONS2026
  3. 3Derivative-free tree optimization for complex systems2024
  4. 4Combinatorial optimization theory and artificial intelligence problems2025
  5. 5Optimized Active Learning Method for High-Dimensional Industrial Regression Problems2025