3 citations · 5 across the 13 of their papers we have counts for
15 papers
Probe-Conditioned Memory for Actuator-Deadband-Aware Koopman MPC in Industrial Sealing
Yue Wu
Industrial sealing and dispensing cells often reuse a pressure chain, nozzle, substrate path, and vision interface across product recipes. For a narrow bead recipe, however, a cali…
Input-to-State Stability Certification via Projection Residuals for Koopman Learning Control of Nonlinear Repetitive Systems
Yue Wu, Ye Cao, Jianfu Cao
This paper studies input-to-state stability (ISS) certification for data-driven Koopman learning control of unknown discrete-time nonlinear repetitive systems over finite trial hor…
ForcingDAS: Unified and Robust Data Assimilation via Diffusion Forcing
Yixuan Jia, Siyi Chen, Yida Pan +9
Data assimilation (DA) estimates the state of an evolving dynamical system from noisy, partial observations, and is widely used in scientific simulation as well as weather and clim…
Diagnostic Certificates of Data Quality and Regression Identifiability for Koopman Identification
Yue Wu
Classical persistent excitation criteria usually assess whether an input or regressor signal is sufficiently rich. In Koopman and EDMD with control (EDMDc), however, data quality i…
Automated Extraction of Mechanical Constitutive Models from Scientific Literature using Large Language Models: Applications in Cultural Heritage Conservation
Rui Hu, Yue Wu, Tianhao Su +3
The preservation of cultural heritage is increasingly transitioning towards data-driven predictive maintenance and "Digital Twin" construction. However, the mechanical constitutive…
Mechanism-Guided Residual Lifting and Control Consistent Modeling for Pneumatic Drying Processes
Yue Wu
Pneumatic drying processes in industries such as agriculture, chemicals,and pharmaceuticals are notoriously difficult to model and control due to multi-source disturbances,coupled…
A Trustworthy Industrial Fault Diagnosis Architecture Integrating Probabilistic Models and Large Language Models
Yue wu
There are limitations of traditional methods and deep learning methods in terms of interpretability, generalization, and quantification of uncertainty in industrial fault diagnosis…
A Data-Driven Prescribed-Time Control Framework via Koopman Operator and Adaptive Backstepping
Yue Wu
Achieving rapid and time-deterministic stabilization for complex systems characterized by strong nonlinearities and parametric uncertainties presents a significant challenge. Tradi…
Online Learning Control Strategies for Industrial Processes with Application for Loosening and Conditioning
Yue Wu, Jianfu Cao, Ye Cao
This paper proposes a novel adaptive Koopman Model Predictive Control (MPC) framework, termed HPC-AK-MPC, designed to address the dual challenges of time-varying dynamics and safe…
Granular Loco-Manipulation: Repositioning Rocks Through Strategic Sand Avalanche
Haodi Hu, Yue Wu, Feifei Qian +1
Legged robots have the potential to leverage obstacles to climb steep sand slopes. However, efficiently repositioning these obstacles to desired locations is challenging. Here we p…
A Convergent ADMM Algorithm for Grain Boundary Energy Minimization
Yue Wu, Luchan Zhang, Yang Xiang
In this paper, we study a constrained minimization problem that arise from materials science to determine the dislocation (line defect) structure of grain boundaries. The problems…
Some Unified Theory for Variance Reduced Prox-Linear Methods
Yue Wu, Benjamin Grimmer
This work considers the nonconvex, nonsmooth problem of minimizing a composite objective of the form where the inner mapping is a smooth finite summation or expe…
Why does the two-timescale Q-learning converge to different mean field solutions? A unified convergence analysis
Jing An, Jianfeng Lu, Yue Wu +1
We revisit the unified two-timescale Q-learning algorithm as initially introduced by Angiuli et al. \cite{angiuli2022unified}. This algorithm demonstrates efficacy in solving mean…
Targeted materials discovery using Bayesian algorithm execution
Sathya Chitturi, Akash Ramdas, Yue Wu +8
Rapid discovery and synthesis of new materials requires intelligent data acquisition strategies to navigate large design spaces. A popular strategy is Bayesian optimization, which…
Nearly Optimal Approximation Rates for Deep Super ReLU Networks on Sobolev Spaces
Yahong Yang, Yue Wu, Haizhao Yang +1
This paper introduces deep super ReLU networks (DSRNs) as a method for approximating functions in Sobolev spaces measured by Sobolev norms for with $m\ge…