6 citations · 10 across the 10 of their papers we have counts for
8 papers · 1 filter
A Koopman--Polynomial Chaos Framework for SMPC of Nonlinear Stochastic Processes
Wallace Gian Yion Tan, Efstathios Iliakis, Liang Wu +2
This paper develops a polynomial parametric Koopman operator (PPKO) framework for stochastic model predictive control (SMPC) of nonlinear systems with parametric uncertainty and ti…
Physics-Informed Neural Network Surrogates with Polynomial Chaos-Based Uncertainty Propagation for Stochastic Model Predictive Control
Srimanta Santra, Romi Patel, Saikat Mukherjee +2
Stochastic partial differential equations (PDEs) govern critical engineering and geophysical systems but are challenging to use for real-time control under parametric uncertainty.…
Improving Performance in Classification Tasks with LCEN and the Weighted Focal Differentiable MCC Loss
Pedro Seber, Richard D. Braatz
The LASSO-Clip-EN (LCEN) algorithm was previously introduced for nonlinear, interpretable feature selection and machine learning. However, its design and use was limited to regress…
Fixed-time-stable ODE Representation of Lasso
Liang Wu, Yunhong Che, Wallace Gian Yion Tan +3
Lasso problems arise in many areas, including signal processing, machine learning, and control, and are closely connected to sparse coding mechanisms observed in neuroscience. A co…
Polynomial Parametric Koopman Operators for Stochastic MPC
Efstathios Iliakis, Wallace Gian Yion Tan, Liang Wu +2
This paper develops a parametric Koopman operator framework for Stochastic Model Predictive Control (SMPC), where the Koopman operator is parametrized by Polynomial Chaos Expansion…
LQR for Systems with Probabilistic Parametric Uncertainties: A Gradient Method
Leilei Cui, Richard D. Braatz
A gradient-based method is proposed for solving the linear quadratic regulator (LQR) problem for linear systems with nonlinear dependence on time-invariant probabilistic parametric…
Koopman-BoxQP: Solving Large-Scale NMPC at kHz Rates
Liang Wu, Wallace Gian Yion Tan, Richard D. Braatz +1
Solving large-scale nonlinear model predictive control (NMPC) problems at kilohertz (kHz) rates on standard processors remains a formidable challenge. This paper proposes a Koopman…
GlycoPy: A CasADi-based Python Framework for Hierarchical Modeling, Optimization, and Control of Bioprocesses
Yingjie Ma, Jing Guo, Richard D. Braatz
Efficient implementation of nonlinear model predictive control (NMPC) for bioprocesses remains challenging because large nonlinear models are difficult to organize, simulate, and e…