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06/24/2023
Physics-Informed Machine Learning for Modeling and Control of Dynamical Systems
Physics-informed machine learning (PIML) is a set of methods and tools t...
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01/28/2023
Violation-Aware Contextual Bayesian Optimization for Controller Performance Optimization with Unmodeled Constraints
We study the problem of performance optimization of closed-loop control ...
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11/14/2022
Meta-Learning of Neural State-Space Models Using Data From Similar Systems
Deep neural state-space models (SSMs) provide a powerful tool for modeli...
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10/31/2022
Optimizing Closed-Loop Performance with Data from Similar Systems: A Bayesian Meta-Learning Approach
Bayesian optimization (BO) has demonstrated potential for optimizing con...
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10/14/2021
VABO: Violation-Aware Bayesian Optimization for Closed-Loop Control Performance Optimization with Unmodeled Constraints
We study the problem of performance optimization of closed-loop control ...
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06/29/2021
Attentive Neural Processes and Batch Bayesian Optimization for Scalable Calibration of Physics-Informed Digital Twins
Physics-informed dynamical system models form critical components of dig...
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05/12/2020
Safe Learning-based Observers for Unknown Nonlinear Systems using Bayesian Optimization
Data generated from dynamical systems with unknown dynamics enable the l...
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07/03/2019
Safe Approximate Dynamic Programming Via Kernelized Lipschitz Estimation
We develop a method for obtaining safe initial policies for reinforcemen...
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06/26/2019