5 citations · 5 across the 5 of their papers we have counts for
5 papers
Weight-Parameterization in Continuous Time Deep Neural Networks for Surrogate Modeling
Haley Rosso, Lars Ruthotto, Khachik Sargsyan
Continuous-time deep learning models, such as neural ordinary differential equations (ODEs), offer a promising framework for surrogate modeling of complex physical systems. A centr…
Neural Network Approaches for Parameterized Optimal Control
Deepanshu Verma, Nick Winovich, Lars Ruthotto +1
We consider numerical approaches for deterministic, finite-dimensional optimal control problems whose dynamics depend on unknown or uncertain parameters. We seek to amortize the so…
Differential Equations for Continuous-Time Deep Learning
Lars Ruthotto
This short, self-contained article seeks to introduce and survey continuous-time deep learning approaches that are based on neural ordinary differential equations (neural ODEs). It…
Learning Control Policies of Hodgkin-Huxley Neuronal Dynamics
Malvern Madondo, Deepanshu Verma, Lars Ruthotto +1
We present a neural network approach for closed-loop deep brain stimulation (DBS). We cast the problem of finding an optimal neurostimulation strategy as a control problem. In this…
Efficient Neural Network Approaches for Conditional Optimal Transport with Applications in Bayesian Inference
Zheyu Oliver Wang, Ricardo Baptista, Youssef Marzouk +2
We present two neural network approaches that approximate the solutions of static and dynamic $\unicode{x1D450}\unicode{x1D45C}\unicode{x1D45B}\unicode{x1D451}\unicode{x1D456}\unic…