58 citations · 108 across the 4 of their papers we have counts for
5 papers · 1 filter
When Physics Meets Machine Learning: A Survey of Physics-Informed Machine Learning
Chuizheng Meng, Sungyong Seo, Defu Cao +2
Physics-informed machine learning (PIML), referring to the combination of prior knowledge of physics, which is the high level abstraction of natural phenomenons and human behaviour…
Controlling Neural Networks with Rule Representations
Sungyong Seo, Sercan O. Arik, Jinsung Yoon +3
We propose a novel training method that integrates rules into deep learning, in a way the strengths of the rules are controllable at inference. Deep Neural Networks with Controllab…
Physics-aware Spatiotemporal Modules with Auxiliary Tasks for Meta-Learning
Sungyong Seo, Chuizheng Meng, Sirisha Rambhatla +1
Modeling the dynamics of real-world physical systems is critical for spatiotemporal prediction tasks, but challenging when data is limited. The scarcity of real-world data and the…
Differentiable Physics-informed Graph Networks
Sungyong Seo, Yan Liu
While physics conveys knowledge of nature built from an interplay between observations and theory, it has been considered less importantly in deep neural networks. Especially, ther…
CSI: A Hybrid Deep Model for Fake News Detection
Natali Ruchansky, Sungyong Seo, Yan Liu
The topic of fake news has drawn attention both from the public and the academic communities. Such misinformation has the potential of affecting public opinion, providing an opport…