21 papers
ML-Guided Primal Heuristics for Mixed Binary Quadratic Programs
Weimin Huang, Natalie M. Isenberg, Ján DrgoÅa +2
Mixed Binary Quadratic Programs (MBQPs) are an important and complex set of problems in combinatorial optimization. As solving large-scale combinatorial optimization problems is ch…
CINOC: Cardinality-Invariant Neural Operator Policies for Scalable PDE Control
Pietro Zanotta, Dibakar Roy Sarkar, Honghui Zheng +2
Controlling partial differential equations (PDEs) with learning-based policies remains fundamentally limited by fixed-dimensional representations: policies trained for a specific s…
MPC: A Parallel-in-horizon and Construction-free NMPC Solver
Liang Wu, Bo Yang, Junheng Li +5
The alternating direction method of multipliers (ADMM) has gained increasing popularity in embedded model predictive control (MPC) due to its code simplicity and pain-free paramete…
Learning to Control PDEs with Differentiable Predictive Control and Time-Integrated Neural Operators
Dibakar Roy Sarkar, Ján DrgoÅa, Somdatta Goswami
We present a data-driven control framework for partial differential equations (PDEs). Our approach integrates Time-Integrated Deep Operator Networks (TI-DeepONets) as differentiabl…
Zero-Shot Function Encoder-Based Differentiable Predictive Control
Hassan Iqbal, Xingjian Li, Tyler Ingebrand +4
We introduce a differentiable framework for zero-shot adaptive control over parametric families of nonlinear dynamical systems. Our approach integrates a function encoder-based neu…
Stability Enhanced Gaussian Process Variational Autoencoders
Carl R. Richardson, Jichen Zhang, Ethan King +1
A novel stability-enhanced Gaussian process variational autoencoder (SEGP-VAE) is proposed for indirectly training a low-dimensional linear time invariant (LTI) system, using high-…