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Found 10 papers, 3 papers with code
Guaranteed approximations of arbitrarily quantified reachability problems
We propose an approach to compute inner and outer-approximations of the sets of values satisfying constraints expressed as arbitrarily quantified formulas.
Taylor-Lagrange Neural Ordinary Differential Equations: Toward Fast Training and Evaluation of Neural ODEs
Neural ordinary differential equations (NODEs) -- parametrizations of differential equations using neural networks -- have shown tremendous promise in learning models of unknown continuous-time dynamical systems from data.
Neural Networks with Physics-Informed Architectures and Constraints for Dynamical Systems Modeling
The physics-informed constraints are enforced via the augmented Lagrangian method during the model's training.
Static analysis of ReLU neural networks with tropical polyhedra
This paper studies the problem of range analysis for feedforward neural networks, which is a basic primitive for applications such as robustness of neural networks, compliance to specifications and reachability analysis of neural-network feedback systems.
Neural Network Based Model Predictive Control for an Autonomous Vehicle
We study learning based controllers as a replacement for model predictive controllers (MPC) for the control of autonomous vehicles.
Reinforcement Learning with Formal Performance Metrics for Quadcopter Attitude Control under Non-nominal Contexts
We explore the reinforcement learning approach to designing controllers by extensively discussing the case of a quadcopter attitude controller.
Tractable higher-order under-approximating AE extensions for non-linear systems
We consider the problem of under and over-approximating the image of general vector-valued functions over bounded sets, and apply the proposed solution to the estimation of reachable sets of uncertain non-linear discrete-time dynamical systems.
Reasoning about Uncertainties in Discrete-Time Dynamical Systems using Polynomial Forms.
In this paper, we propose polynomial forms to represent distributions of state variables over time for discrete-time stochastic dynamical systems.
On-The-Fly Control of Unknown Systems: From Side Information to Performance Guarantees through Reachability
Besides, $\texttt{DaTaControl}$ achieves near-optimal control and is suitable for real-time control of such systems.
On-The-Fly Control of Unknown Smooth Systems from Limited Data
We investigate the problem of data-driven, on-the-fly control of systems with unknown nonlinear dynamics where data from only a single finite-horizon trajectory and possibly side information on the dynamics are available.
