45 citations · 73 across the 9 of their papers we have counts for
4 papers · 2 filters
Bridging Physics-based and Data-driven modeling for Learning Dynamical Systems
Rui Wang, Danielle Maddix, Christos Faloutsos +2
How can we learn a dynamical system to make forecasts, when some variables are unobserved? For instance, in COVID-19, we want to forecast the number of infected and death cases but…
Aortic Pressure Forecasting with Deep Sequence Learning
Eliza Huang, Rui Wang, Uma Chandrasekaran +1
Mean aortic pressure (MAP) is a major determinant of perfusion in all organs systems. The ability to forecast MAP would enhance the ability of physicians to estimate prognosis of t…
Estimating Q(s,s') with Deep Deterministic Dynamics Gradients
Ashley D. Edwards, Himanshu Sahni, Rosanne Liu +7
In this paper, we introduce a novel form of value function, , that expresses the utility of transitioning from a state to a neighboring state and then acting opt…
Incorporating Symmetry into Deep Dynamics Models for Improved Generalization
Rui Wang, Robin Walters, Rose Yu
Recent work has shown deep learning can accelerate the prediction of physical dynamics relative to numerical solvers. However, limited physical accuracy and an inability to general…