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Found 23 papers, 10 papers with code
BOIS: Bayesian Optimization of Interconnected Systems
We show that this simple approach (which we call BOIS) enables the exploitation of structural knowledge, such as that arising in interconnected systems as well as systems that embed multiple GP models and combinations of physics and GP models.
Uncertainty Quantification for Molecular Property Predictions with Graph Neural Architecture Search
To that end, we introduce AutoGNNUQ, an automated uncertainty quantification (UQ) approach for molecular property prediction.
Quantifying Space-Time Load Shifting Flexibility in Electricity Markets
The power grid is undergoing significant restructuring driven by the adoption of wind/solar power and the incorporation of new flexible technologies that can shift load in space and time (e. g., data centers, battery storage, and modular manufacturing).
A Graph-Based Modeling Framework for Tracing Hydrological Pollutant Transport in Surface Waters
We demonstrate the capabilities of our framework by using case studies in the State of Wisconsin; here, we aim to identify upstream nutrient pollutant sources that arise from agricultural practices and trace downstream impacts to waterbodies, rivers, and streams.
Scalable Primal Decomposition Schemes for Large-Scale Infrastructure Networks
The real-time operation of large-scale infrastructure networks requires scalable optimization capabilities.
Convolutional Neural Networks: Basic Concepts and Applications in Manufacturing
We discuss basic concepts of convolutional neural networks (CNNs) and outline uses in manufacturing.
New Paradigms for Exploiting Parallel Experiments in Bayesian Optimization
However, a key limitation of BO is that it is an inherently sequential algorithm (one experiment is proposed per round) and thus cannot directly exploit high-throughput (parallel) experiments.
Pricing and Remunerating Electricity Storage Flexibility Using Virtual Links
Ambitious renewable portfolio standards motivate the incorporation of energy storage resources (ESR) as sources of flexibility.
Exploring the Impacts of Power Grid Signals on Data Center Operations using a Receding-Horizon Scheduling Model
An obstacle that arises here is the lack of computationally-tractable DC operation models that can capture objectives, constraints, and information flows that arise at the interface of DCs and the power grid.
SAFE-OCC: A Novelty Detection Framework for Convolutional Neural Network Sensors and its Application in Process Control
We show that this framework enables the safe use of computer vision sensors in process control architectures.
Remunerating Space-Time, Load-Shifting Flexibility from Data Centers in Electricity Markets
Central to our study is the concept of virtual links, which provide non-physical pathways that can be used by DaCes to shift power loads (by shifting computing loads) across space and time.
Convolutional Neural Nets in Chemical Engineering: Foundations, Computations, and Applications
CNNs highlight features from the grid data by performing convolution operations with different types of operators.
Exponential Decay of Sensitivity in Graph-Structured Nonlinear Programs
We study solution sensitivity for nonlinear programs (NLPs) whose structures are induced by graphs.
Stochastic Optimization
Optimization and Control
A Dynamic Penalty Function Approach for Constraints-Handling in Reinforcement Learning
We first examine the ability of a neural network to represent a value function when uniform, linear, or DP functions are added to prevent constraint violation.
Mitigating Investment Risk Using Modular Technologies
We study logistical investment flexibility provided by modular processing technologies for mitigating risk.
Optimization and Control
SBML2Julia: interfacing SBML with efficient nonlinear Julia modelling and solution tools for parameter optimization
Motivation: Estimating model parameters from experimental observations is one of the key challenges in systems biology and can be computationally very expensive.
MPC Controller Tuning using Bayesian Optimization Techniques
The approach is motivated by the observation that evaluating the closed-loop performance of MPC by trial-and-error is time-consuming (e. g., every closed-loop simulation can involve solving thousands of optimization problems).
Image-Based Model Predictive Control via Dynamic Mode Decomposition
We show that the dynamics of this high-dimensional space can be accurately predicted by using a 40-dimensional DMD model and we show that the field can be manipulated satisfactorily by using an MPC controller that embeds the low-dimensional DMD model.
A Graph-Based Modeling Abstraction for Optimization: Concepts and Implementation in Plasmo.jl
We present a general graph-based modeling abstraction for optimization that we call an OptiGraph.
Optimization and Control
On the Convergence of Overlapping Schwarz Decomposition for Nonlinear Optimal Control
We study the convergence properties of an overlapping Schwarz decomposition algorithm for solving nonlinear optimal control problems (OCPs).
Unifying Theorems for Subspace Identification and Dynamic Mode Decomposition
This paper presents unifying results for subspace identification (SID) and dynamic mode decomposition (DMD) for autonomous dynamical systems.
On the Convergence of the Dynamic Inner PCA Algorithm
In this work, we show that this algorithm is a specialized variant of a coordinate maximization algorithm.
Graph-Based Modeling and Simulation of Complex Systems
We present graph-based modeling abstractions to represent cyber-physical dependencies arising in complex systems.
Optimization and Control
