37 citations · 53 across the 5 of their papers we have counts for
5 papers
Deep Hierarchical Knowledge Loss for Fault Intensity Diagnosis
Yu Sha, Shuiping Gou, Bo Liu +8
Fault intensity diagnosis (FID) plays a pivotal role in intelligent manufacturing while neglecting dependencies among target classes hinders its practical deployment. This paper in…
Hierarchical knowledge guided fault intensity diagnosis of complex industrial systems
Yu Sha, Shuiping Gou, Bo Liu +9
Fault intensity diagnosis (FID) plays a pivotal role in monitoring and maintaining mechanical devices within complex industrial systems. As current FID methods are based on chain o…
Physics-Driven Learning for Inverse Problems in Quantum Chromodynamics
Gert Aarts, Kenji Fukushima, Tetsuo Hatsuda +4
The integration of deep learning techniques and physics-driven designs is reforming the way we address inverse problems, in which accurate physical properties are extracted from co…
Resource-aware Research on Universe and Matter: Call-to-Action in Digital Transformation
Ben Bruers, Marilyn Cruces, Markus Demleitner +34
Given the urgency to reduce fossil fuel energy production to make climate tipping points less likely, we call for resource-aware knowledge gain in the research areas on Universe an…
Machine learning study to identify collective flow in small and large colliding systems
Shuang Guo, Han-Sheng Wang, Kai Zhou +1
Collective flow has been found to be similar between small colliding systems ( and A collisions) and large colliding systems (peripheral A A collisions) at t…