activity
20182022
most citedScalable Differentiable Physics for Learning and Control

31 citations · 70 across the 8 of their papers we have counts for

collaborators

9 papers

quant-ph20225 cited

Differentiable Analog Quantum Computing for Optimization and Control

Jiaqi Leng, Yuxiang Peng, Yi-Ling Qiao +2

We formulate the first differentiable analog quantum computing framework with a specific parameterization design at the analog signal (pulse) level to better exploit near-term quan…

cs.CV20228 cited

NeuPhysics: Editable Neural Geometry and Physics from Monocular Videos

Yi-Ling Qiao, Alexander Gao, Ming C. Lin

We present a method for learning 3D geometry and physics parameters of a dynamic scene from only a monocular RGB video input. To decouple the learning of underlying scene geometry…

cs.GR2022

Differentiable Hybrid Traffic Simulation

Sanghyun Son, Yi-Ling Qiao, Jason Sewall +1

We introduce a novel differentiable hybrid traffic simulator, which simulates traffic using a hybrid model of both macroscopic and microscopic models and can be directly integrated…

cs.LG202210 cited

Differentiable Simulation of Soft Multi-body Systems

Yi-Ling Qiao, Junbang Liang, Vladlen Koltun +1

We present a method for differentiable simulation of soft articulated bodies. Our work enables the integration of differentiable physical dynamics into gradient-based pipelines. We…

cs.LG20216 cited

Efficient Differentiable Simulation of Articulated Bodies

Yi-Ling Qiao, Junbang Liang, Vladlen Koltun +1

We present a method for efficient differentiable simulation of articulated bodies. This enables integration of articulated body dynamics into deep learning frameworks, and gradient…

cs.LG202031 cited

Scalable Differentiable Physics for Learning and Control

Yi-Ling Qiao, Junbang Liang, Vladlen Koltun +1

Differentiable physics is a powerful approach to learning and control problems that involve physical objects and environments. While notable progress has been made, the capabilitie…