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04/06/2023
Unconstrained Parametrization of Dissipative and Contracting Neural Ordinary Differential Equations
In this work, we introduce and study a class of Deep Neural Networks (DN...
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03/21/2023
Universal Approximation Property of Hamiltonian Deep Neural Networks
This paper investigates the universal approximation capabilities of Hami...
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11/14/2022
Follow the Clairvoyant: an Imitation Learning Approach to Optimal Control
We consider control of dynamical systems through the lens of competitive...
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11/11/2022
Physically Consistent Neural ODEs for Learning Multi-Physics Systems
Despite the immense success of neural networks in modeling system dynami...
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10/05/2022
Maximum likelihood estimation of distribution grid topology and parameters from smart meter data
This paper defines a Maximum Likelihood Estimator (MLE) for the admittan...
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04/12/2022
Robust online joint state/input/parameter estimation of linear systems
This paper presents a method for jointly estimating the state, input, an...
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03/22/2022
Neural System Level Synthesis: Learning over All Stabilizing Policies for Nonlinear Systems
We address the problem of designing stabilizing control policies for non...
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03/22/2022
On Robust Classification using Contractive Hamiltonian Neural ODEs
Deep neural networks can be fragile and sensitive to small input perturb...
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12/16/2021
Distributed neural network control with dependability guarantees: a compositional port-Hamiltonian approach
Large-scale cyber-physical systems require that control policies are dis...
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07/09/2021
Bayesian Error-in-Variables Models for the Identification of Power Networks
The increasing integration of intermittent renewable generation, especia...
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05/27/2021
Hamiltonian Deep Neural Networks Guaranteeing Non-vanishing Gradients by Design
Deep Neural Networks (DNNs) training can be difficult due to vanishing a...
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04/27/2021
A unified framework for Hamiltonian deep neural networks
Training deep neural networks (DNNs) can be difficult due to the occurre...
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03/13/2020
