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Lotka-Volterra, Glycolytic Oscillator, and Gray-Scott problems
The dataset used in the paper is a collection of trajectories from different environments, each with its own set of parameters. -
Physics-informed Graph Neural Networks for Dynamical Systems
The dataset used in this paper is a collection of simulated trajectories of physical systems, including n-pendulum, n-spring, 4-body gravitational system, and rigid-body system. -
Discovery of Dynamics via Deep Learning
The dataset used in the paper is a collection of time series data from a dynamical system, which is used to test the performance of the network-based LMMs for discovering... -
State Space Representations of Deep Neural Networks
This paper deals with neural networks as dynamical systems governed by finite difference equations. It shows that the introduction of k-many skip connections into network... -
Synthesis of Neural Barrier Certiļ¬cates
The dataset used in the paper is a set of polynomial and non-polynomial dynamical models, including the Darboux model, the exponential model, the obstacle avoidance problem, the... -
Neural Dynamical Systems: Balancing Structure and Flexibility in Physical Pre...
A dataset of synthetic dynamical systems, including the Lorenz system and a generalized cartpole problem, used to evaluate the performance of the Neural Dynamical Systems (NDS)... -
Unknown Dynamical Systems
The dataset is used to test the proposed generalized residue network (gResNet) framework for learning unknown governing equations from observational data.
