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Found 9 papers, 4 papers with code
When Physics Meets Machine Learning: A Survey of Physics-Informed Machine Learning
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 behaviours in the long history, with data-driven machine learning models, has emerged as an effective way to mitigate the shortage of training data, to increase models' generalizability and to ensure the physical plausibility of results.
BIG-bench Machine Learning
Physics-informed machine learning
Controlling Neural Networks with Rule Representations
The key aspect of DeepCTRL is that it does not require retraining to adapt the rule strength -- at inference, the user can adjust it based on the desired operation point on accuracy vs. rule verification ratio.
Network Inference from a Mixture of Diffusion Models for Fake News Mitigation
Users influential in the propagation of true and fake contents are identified using the inferred diffusion dynamics.
Physics-aware Spatiotemporal Modules with Auxiliary Tasks for Meta-Learning
Although the knowledge of governing partial differential equations (PDE) of data can be helpful for the fast adaptation to few observations, it is mostly infeasible to exactly find the equation for observations in real-world physical systems.
COVID-19 on Social Media: Analyzing Misinformation in Twitter Conversations
The analysis is presented and updated on a publically accessible dashboard (https://usc-melady. github. io/COVID-19-Tweet-Analysis) to track the nature of online discourse and misinformation about COVID-19 on Twitter from March 1 - June 5, 2020.
A Deep Structural Model for Analyzing Correlated Multivariate Time Series
The seasonality component is approximated via a non-liner function of a set of Fourier terms, and the event components are learned by a simple linear function of regressor encoding the event dates.
Differentiable Physics-informed Graph Networks
While physics conveys knowledge of nature built from an interplay between observations and theory, it has been considered less importantly in deep neural networks.
Automatically Inferring Data Quality for Spatiotemporal Forecasting
Spatiotemporal forecasting has become an increasingly important prediction task in machine learning and statistics due to its vast applications, such as climate modeling, traffic prediction, video caching predictions, and so on.
CSI: A Hybrid Deep Model for Fake News Detection
Specifically, we incorporate the behavior of both parties, users and articles, and the group behavior of users who propagate fake news.
