118 citations · 126 across the 6 of their papers we have counts for
6 papers
Posterior Sampling with Denoising Oracles via Tilted Transport
Joan Bruna, Jiequn Han
Score-based diffusion models have significantly advanced high-dimensional data generation across various domains, by learning a denoising oracle (or score) from datasets. From a Ba…
Improving Gradient Computation for Differentiable Physics Simulation with Contacts
Yaofeng Desmond Zhong, Jiequn Han, Biswadip Dey +1
Differentiable simulation enables gradients to be back-propagated through physics simulations. In this way, one can learn the dynamics and properties of a physics system by gradien…
Pandemic Control, Game Theory and Machine Learning
Yao Xuan, Robert Balkin, Jiequn Han +2
Game theory has been an effective tool in the control of disease spread and in suggesting optimal policies at both individual and area levels. In this AMS Notices article, we focus…
Differentiable Physics Simulations with Contacts: Do They Have Correct Gradients w.r.t. Position, Velocity and Control?
Yaofeng Desmond Zhong, Jiequn Han, Georgia Olympia Brikis
In recent years, an increasing amount of work has focused on differentiable physics simulation and has produced a set of open source projects such as Tiny Differentiable Simulator,…
Neural-Network Quantum States for Periodic Systems in Continuous Space
Gabriel Pescia, Jiequn Han, Alessandro Lovato +2
We introduce a family of neural quantum states for the simulation of strongly interacting systems in the presence of spatial periodicity. Our variational state is parameterized in…
Deep Learning Approximation for Stochastic Control Problems
Jiequn Han, Weinan E
Many real world stochastic control problems suffer from the "curse of dimensionality". To overcome this difficulty, we develop a deep learning approach that directly solves high-di…