1 code implementation • 22 Sep 2021 • Yunkai Wang, Dongkun Zhang, Yuxiang Cui, Zexi Chen, Wei Jing, Junbo Chen, Rong Xiong, Yue Wang
In this paper, we propose a domain generalization method for vision-based driving trajectory generation for autonomous vehicles in urban environments, which can be seen as a solution to extend the Invariant Risk Minimization (IRM) method in complex problems.
2 code implementations • 20 Oct 2020 • Yunkai Wang, Dongkun Zhang, Jingke Wang, Zexi Chen, Yue Wang, Rong Xiong
One of the challenges to reduce the gap between the machine and the human level driving is how to endow the system with the learning capacity to deal with the coupled complexity of environments, intentions, and dynamics.
Robotics
1 code implementation • 8 May 2020 • Jingke Wang, Yue Wang, Dongkun Zhang, Yezhou Yang, Rong Xiong
To improve the tactical decision-making for learning-based driving solution, we introduce hierarchical behavior and motion planning (HBMP) to explicitly model the behavior in learning-based solution.
no code implementations • 23 Sep 2019 • Xuhui Meng, Zhen Li, Dongkun Zhang, George Em. Karniadakis
Consequently, compared to the original PINN approach, the proposed PPINN approach may achieve a significant speedup for long-time integration of PDEs, assuming that the CG solver is fast and can provide reasonable predictions of the solution, hence aiding the PPINN solution to converge in just a few iterations.
no code implementations • 3 May 2019 • Dongkun Zhang, Ling Guo, George Em. Karniadakis
One of the open problems in scientific computing is the long-time integration of nonlinear stochastic partial differential equations (SPDEs).
no code implementations • 5 Nov 2018 • Liu Yang, Dongkun Zhang, George Em. Karniadakis
We developed a new class of physics-informed generative adversarial networks (PI-GANs) to solve in a unified manner forward, inverse and mixed stochastic problems based on a limited number of scattered measurements.
no code implementations • 21 Sep 2018 • Dongkun Zhang, Lu Lu, Ling Guo, George Em. Karniadakis
Here, we propose a new method with the objective of endowing the DNN with uncertainty quantification for both sources of uncertainty, i. e., the parametric uncertainty and the approximation uncertainty.