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08/24/2023
Extreme Risk Mitigation in Reinforcement Learning using Extreme Value Theory
Risk-sensitive reinforcement learning (RL) has garnered significant atte...
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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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04/21/2023
Power Grid Behavioral Patterns and Risks of Generalization in Applied Machine Learning
Recent years have seen a rich literature of data-driven approaches desig...
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12/27/2022
Proceedings of AAAI 2022 Fall Symposium: The Role of AI in Responding to Climate Challenges
Climate change is one of the most pressing challenges of our time, requi...
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11/27/2022
Machine Learning for Smart and Energy-Efficient Buildings
Energy consumption in buildings, both residential and commercial, accoun...
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08/15/2022
Domain-aware Control-oriented Neural Models for Autonomous Underwater Vehicles
Conventional physics-based modeling is a time-consuming bottleneck in co...
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08/03/2022
Differentiable Predictive Control with Safety Guarantees: A Control Barrier Function Approach
We develop a novel form of differentiable predictive control (DPC) with ...
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07/11/2022
Structural Inference of Networked Dynamical Systems with Universal Differential Equations
Networked dynamical systems are common throughout science in engineering...
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05/22/2022
Neural Lyapunov Differentiable Predictive Control
We present a learning-based predictive control methodology using the dif...
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03/20/2022
Neuro-physical dynamic load modeling using differentiable parametric optimization
In this work, we investigate a data-driven approach for obtaining a redu...
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03/02/2022
Learning Stochastic Parametric Differentiable Predictive Control Policies
The problem of synthesizing stochastic explicit model predictive control...
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02/28/2022
Neural Ordinary Differential Equations for Nonlinear System Identification
Neural ordinary differential equations (NODE) have been recently propose...
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11/08/2021
On the Stochastic Stability of Deep Markov Models
Deep Markov models (DMM) are generative models that are scalable and exp...
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11/02/2021
Constructing Neural Network-Based Models for Simulating Dynamical Systems
Dynamical systems see widespread use in natural sciences like physics, b...
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07/25/2021
Deep Learning Explicit Differentiable Predictive Control Laws for Buildings
We present a differentiable predictive control (DPC) methodology for lea...
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01/06/2021
Constrained Block Nonlinear Neural Dynamical Models
Neural network modules conditioned by known priors can be effectively tr...
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12/15/2020
LSTM-based Space Occupancy Prediction towards Efficient Building Energy Management
Energy consumed in buildings takes significant portions of the total glo...
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11/26/2020
Physics-Informed Neural State Space Models via Learning and Evolution
Recent works exploring deep learning application to dynamical systems mo...
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11/26/2020
Spectral Analysis and Stability of Deep Neural Dynamics
Our modern history of deep learning follows the arc of famous emergent d...
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11/11/2020
Physics-constrained Deep Learning of Multi-zone Building Thermal Dynamics
We present a physics-constrained control-oriented deep learning method f...
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04/23/2020
Constrained Physics-Informed Deep Learning for Stable System Identification and Control of Unknown Linear Systems
This paper presents a novel data-driven method for learning deep constra...
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04/23/2020
Constrained Physics-Informed Deep Learning for Stable System Identification and Control of Linear Systems
This paper presents a novel data-driven method for learning deep constra...
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04/22/2020