Synapse
⌘+K
Synapse
PulseExploreClubsResearchersJournals
Instagram
HomeClubsExplore
April 7, 2024Open Access

Physics-informed Machine Learning for Predicting Fatigue Damage of Wire Bonds in Power Electronic Modules

View Full Paper
Ask AI
Bookmark
Share

Authors

SSStoyan StoyanovTTT. TilfordXZXiaotian Zhang

Discussion

Loading...

Member takes

Overview

Key Points

Key points are not available for this paper at this time.

Cite This Study

Stoyanov et al. (2024) studied this question.

synapsesocial.com/papers/68e701f4b6db64358767bc6bhttps://doi.org/10.1109/eurosime60745.2024.10491522
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. 1Reliability Meta-modelling of Power Components2024
  2. 2Data-Driven and Hybrid Modeling for Metal Fatigue: A Review of Classical Methods, Machine Learning, and Physics-Informed Neural Networks2026 · 1 citations
  3. 3Hybrid modeling for remaining useful life prediction in power module prognosis2024
  4. 4Mechanistic machine learning for metamaterial fatigue strength design from first principles in additive manufacturing2024 · 10 citations
  5. 5Physics‐Based Machine Learning for Modeling Cyclic Damage Evolution2026