93 citations · 272 across the 14 of their papers we have counts for
15 papers
Learning Preconditioner for Conjugate Gradient PDE Solvers
Yichen Li, Peter Yichen Chen, Tao Du +1
Efficient numerical solvers for partial differential equations empower science and engineering. One of the commonly employed numerical solvers is the preconditioned conjugate gradi…
Learning Neural Constitutive Laws From Motion Observations for Generalizable PDE Dynamics
Pingchuan Ma, Peter Yichen Chen, Bolei Deng +4
We propose a hybrid neural network (NN) and PDE approach for learning generalizable PDE dynamics from motion observations. Many NN approaches learn an end-to-end model that implici…
DexDeform: Dexterous Deformable Object Manipulation with Human Demonstrations and Differentiable Physics
Sizhe Li, Zhiao Huang, Tao Chen +4
In this work, we aim to learn dexterous manipulation of deformable objects using multi-fingered hands. Reinforcement learning approaches for dexterous rigid object manipulation wou…
Fluidic Topology Optimization with an Anisotropic Mixture Model
Yifei Li, Tao Du, Sangeetha Grama Srinivasan +4
Fluidic devices are crucial components in many industrial applications involving fluid mechanics. Computational design of a high-performance fluidic system faces multifaceted chall…
RISP: Rendering-Invariant State Predictor with Differentiable Simulation and Rendering for Cross-Domain Parameter Estimation
Pingchuan Ma, Tao Du, Joshua B. Tenenbaum +2
This work considers identifying parameters characterizing a physical system's dynamic motion directly from a video whose rendering configurations are inaccessible. Existing solutio…
Contact Points Discovery for Soft-Body Manipulations with Differentiable Physics
Sizhe Li, Zhiao Huang, Tao Du +3
Differentiable physics has recently been shown as a powerful tool for solving soft-body manipulation tasks. However, the differentiable physics solver often gets stuck when the ini…
Fast Aquatic Swimmer Optimization with Differentiable Projective Dynamics and Neural Network Hydrodynamic Models
Elvis Nava, John Z. Zhang, Mike Y. Michelis +5
Aquatic locomotion is a classic fluid-structure interaction (FSI) problem of interest to biologists and engineers. Solving the fully coupled FSI equations for incompressible Navier…
Dynamic Visual Reasoning by Learning Differentiable Physics Models from Video and Language
Mingyu Ding, Zhenfang Chen, Tao Du +3
In this work, we propose a unified framework, called Visual Reasoning with Differ-entiable Physics (VRDP), that can jointly learn visual concepts and infer physics models of object…
Sim2Real for Soft Robotic Fish via Differentiable Simulation
John Z. Zhang, Yu Zhang, Pingchuan Ma +5
Accurate simulation of soft mechanisms under dynamic actuation is critical for the design of soft robots. We address this gap with our differentiable simulation tool by learning th…
DiffCloth: Differentiable Cloth Simulation with Dry Frictional Contact
Yifei Li, Tao Du, Kui Wu +2
Cloth simulation has wide applications in computer animation, garment design, and robot-assisted dressing. This work presents a differentiable cloth simulator whose additional grad…
DiffAqua: A Differentiable Computational Design Pipeline for Soft Underwater Swimmers with Shape Interpolation
Pingchuan Ma, Tao Du, John Z. Zhang +4
The computational design of soft underwater swimmers is challenging because of the high degrees of freedom in soft-body modeling. In this paper, we present a differentiable pipelin…
PlasticineLab: A Soft-Body Manipulation Benchmark with Differentiable Physics
Zhiao Huang, Yuanming Hu, Tao Du +4
Simulated virtual environments serve as one of the main driving forces behind developing and evaluating skill learning algorithms. However, existing environments typically only sim…
DiffPD: Differentiable Projective Dynamics
Tao Du, Kui Wu, Pingchuan Ma +4
We present a novel, fast differentiable simulator for soft-body learning and control applications. Existing differentiable soft-body simulators can be classified into two categorie…
Fusion 360 Gallery: A Dataset and Environment for Programmatic CAD Construction from Human Design Sequences
Karl D. D. Willis, Yewen Pu, Jieliang Luo +5
Parametric computer-aided design (CAD) is a standard paradigm used to design manufactured objects, where a 3D shape is represented as a program supported by the CAD software. Despi…
Efficient Continuous Pareto Exploration in Multi-Task Learning
Pingchuan Ma, Tao Du, Wojciech Matusik
Tasks in multi-task learning often correlate, conflict, or even compete with each other. As a result, a single solution that is optimal for all tasks rarely exists. Recent papers i…