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Found 9 papers, 3 papers with code
Differentiable Predictive Control for Large-Scale Urban Road Networks
Transportation is a major contributor to CO2 emissions, making it essential to optimize traffic networks to reduce energy-related emissions.
Physics-Informed Machine Learning for Modeling and Control of Dynamical Systems
Specifically, the paper covers an overview of the theory, fundamental concepts and methods, tools, and applications on topics of: 1) physics-informed learning for system identification; 2) physics-informed learning for control; 3) analysis and verification of PIML models; and 4) physics-informed digital twins.
Proceedings of AAAI 2022 Fall Symposium: The Role of AI in Responding to Climate Challenges
These applications have implications in areas ranging as widely as energy, agriculture, and finance.
Domain-aware Control-oriented Neural Models for Autonomous Underwater Vehicles
Conventional physics-based modeling is a time-consuming bottleneck in control design for complex nonlinear systems like autonomous underwater vehicles (AUVs).
Neural Lyapunov Differentiable Predictive Control
We present a learning-based predictive control methodology using the differentiable programming framework with probabilistic Lyapunov-based stability guarantees.
Learning Stochastic Parametric Differentiable Predictive Control Policies
The problem of synthesizing stochastic explicit model predictive control policies is known to be quickly intractable even for systems of modest complexity when using classical control-theoretic methods.
Neural Ordinary Differential Equations for Nonlinear System Identification
In particular, we present a quantitative study comparing NODE's performance against neural state-space models and classical linear system identification methods.
On the Stochastic Stability of Deep Markov Models
Deep Markov models (DMM) are generative models that are scalable and expressive generalization of Markov models for representation, learning, and inference problems.
Constructing Neural Network-Based Models for Simulating Dynamical Systems
Dynamical systems see widespread use in natural sciences like physics, biology, chemistry, as well as engineering disciplines such as circuit analysis, computational fluid dynamics, and control.
