2 citations · 8 across the 17 of their papers we have counts for
19 papers
To lift or not to lift: Operator inference for nonlinear PDEs with boundary control
Zhuoyuan Wang, Yorie Nakahira, Wael H. Ali +1
Operator inference (OpInf) provides a nonintrusive framework for learning projection-based reduced-order models directly from data, but its standard formulations do not explicitly…
Learnable Splines for Unstructured Neural Operators
Vignesh Gopakumar, Zhuoyuan Wang, Ander Gray +12
Neural operators approximate solution operators of partial differential equations at a fraction of the cost of numerical solvers. Efficient grid-based architectures such as the Fou…
Online Safety Filter for Deformable Object Manipulation with Horizon Agnostic Neural Operators
Jiaxing Li, Hanjiang Hu, Zhuoyuan Wang +2
Safety critical control of robotic manipulation tasks involving deformable media such as fluids, cloth, and soft objects remains challenging because existing learning based approac…
Fractional Risk Analysis of Stochastic Systems with Jumps and Memory
Yimeng Sun, Zhuoyuan Wang, Xiaole Zhang +4
Accurate risk assessment is essential for safety-critical autonomous and control systems under uncertainty. In many real-world settings, stochastic dynamics exhibit asymmetric jump…
OpInf-LLM: Parametric PDE Solving with LLMs via Operator Inference
Zhuoyuan Wang, Hanjiang Hu, Xiyu Deng +2
Solving diverse partial differential equations (PDEs) is fundamental in science and engineering. Large language models (LLMs) have demonstrated strong capabilities in code generati…
Online Adaptive Probabilistic Safety Certificate with Language Guidance
Zhuoyuan Wang, Xiyu Deng, Hikaru Hoshino +1
Achieving long-term safety in uncertain/extreme environments while accounting for human preferences remains a fundamental challenge for autonomous systems. Existing methods often t…
OSGym: Scalable OS Infra for Computer Use Agents
Zengyi Qin, Jinyuan Chen, Yunze Man +25
Training computer use agents requires full-featured OS sandboxes with GUI environments, which consume substantial hardware resources as the number of sandboxes scales. Stochastic e…
Safe Driving in Occluded Environments
Zhuoyuan Wang, Tongyao Jia, Pharuj Rajborirug +5
Ensuring safe autonomous driving in the presence of occlusions poses a significant challenge in its policy design. While existing model-driven control techniques based on set invar…
Multi-Level Multi-Fidelity Methods for Path Integral and Safe Control
Zhuoyuan Wang, Takashi Tanaka, Yongxin Chen +1
Sampling-based approaches are widely used in systems without analytic models to estimate risk or find optimal control. However, gathering sufficient data in such scenarios can be p…
Neural Spline Operators for Risk Quantification in Stochastic Systems
Zhuoyuan Wang, Raffaele Romagnoli, Kamyar Azizzadenesheli +1
Accurately quantifying long-term risk probabilities in diverse stochastic systems is essential for safety-critical control. However, existing sampling-based and partial differentia…
Physics-Informed Deep B-Spline Networks
Zhuoyuan Wang, Raffaele Romagnoli, Saviz Mowlavi +1
Physics-informed machine learning offers a promising framework for solving complex partial differential equations (PDEs) by integrating observational data with governing physical l…
Generalizable Physics-Informed Learning for Stochastic Safety-Critical Systems
Zhuoyuan Wang, Albert Chern, Yorie Nakahira
Accurate estimation of long-term risk is essential for the design and analysis of stochastic dynamical systems. Existing risk quantification methods typically rely on extensive dat…
Myopically Verifiable Probabilistic Certificates for Safe Control and Learning
Zhuoyuan Wang, Haoming Jing, Christian Kurniawan +2
This paper addresses the design of safety certificates for stochastic systems, with a focus on ensuring long-term safety through fast real-time control. In stochastic environments,…
Physics-Informed Representation and Learning: Control and Risk Quantification
Zhuoyuan Wang, Reece Keller, Xiyu Deng +3
Optimal and safety-critical control are fundamental problems for stochastic systems, and are widely considered in real-world scenarios such as robotic manipulation and autonomous d…
MultiModal-Learning for Predicting Molecular Properties: A Framework Based on Image and Graph Structures
Zhuoyuan Wang, Jiacong Mi, Shan Lu +1
The quest for accurate prediction of drug molecule properties poses a fundamental challenge in the realm of Artificial Intelligence Drug Discovery (AIDD). An effective representati…
Towards Proactive Safe Human-Robot Collaborations via Data-Efficient Conditional Behavior Prediction
Ravi Pandya, Zhuoyuan Wang, Yorie Nakahira +1
We focus on the problem of how we can enable a robot to collaborate seamlessly with a human partner, specifically in scenarios where preexisting data is sparse. Much prior work in…
A Generalizable Physics-informed Learning Framework for Risk Probability Estimation
Zhuoyuan Wang, Yorie Nakahira
Accurate estimates of long-term risk probabilities and their gradients are critical for many stochastic safe control methods. However, computing such risk probabilities in real-tim…
Myopically Verifiable Probabilistic Certificate for Long-term Safety
Zhuoyuan Wang, Haoming Jing, Christian Kurniawan +2
In this paper, we consider the use of barrier function-based approaches for the safe control problem in stochastic systems. With the presence of stochastic uncertainties, a myopic…
Self-Supervised Discovering of Interpretable Features for Reinforcement Learning
Wenjie Shi, Gao Huang, Shiji Song +3
Deep reinforcement learning (RL) has recently led to many breakthroughs on a range of complex control tasks. However, the agent's decision-making process is generally not transpare…