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Hare-Lynx population and Coronavirus 2019 outbreak
The dataset used in this paper is a collection of time series data from dynamical systems, including the Hare-Lynx population and Coronavirus 2019 outbreak. -
Neural Abstractions for Dynamical Models
The dataset used in this paper is a collection of neural abstractions for dynamical models. The dataset consists of neural networks with different activation functions... -
Simple Pendulum (SP) problem
The dataset used in the paper is a collection of trajectories from different environments, each with its own set of parameters. -
Gray-Scott problem
The dataset used in the paper is a collection of trajectories from different environments, each with its own set of parameters. -
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. -
Van der Pol oscillator dataset
The dataset used in this paper is a collection of N input-output pairs (xj, y∗j) ∈ Rn × Rm, where N is a positive integer. The inputs xj are randomly generated from a continuous... -
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... -
Kuramoto-Sivashinsky Equation
The Kuramoto-Sivashinsky equation is a model for spatiotemporal chaos. The Kuramoto-Sivashinsky equation is expressed as: yt = −yyx − yxx − yxxxx -
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 Certificates
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... -
Damped Harmonic Oscillator with Displacement-Dependent Damping and Restoring ...
The dataset consists of a damped harmonic oscillator with a displacement-dependent damping and restoring force, where the governing equation is given by¨y = −c1 ˙y − k1 y + 0.1t... -
Damped Harmonic Oscillator with Time-Dependent Forcing
The dataset consists of a damped harmonic oscillator with a time-dependent forcing function, where the governing equation is given by m¨y = −c ˙y − ky + F(t). -
Van der Pol Oscillator
The Van der Pol oscillator is a nonlinear oscillator that is widely used in various fields such as physics, engineering, and control theory. -
Lorenz System
The Lorenz system is a well-known nonlinear dynamical system to model convective hydrodynamic flows. The consequence of chaos is that, under certain parameterizations, the... -
Dynamical Systems Dataset
The dataset used in the paper consists of 5 types of dynamical systems: Heat, MAK, MM, PD, and SIS. -
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)... -
Lorenz-63 System
The dataset used in this paper is a collection of time series data from various dynamical systems, including the harmonic oscillator, Duffing system, Van der Pol oscillator, and... -
Harmonic Oscillator, Duffing System, Van der Pol Oscillator, Lorenz-63 System
The dataset used in this paper is a collection of time series data from various dynamical systems, including the harmonic oscillator, Duffing system, Van der Pol oscillator, and... -
Unknown Dynamical Systems
The dataset is used to test the proposed generalized residue network (gResNet) framework for learning unknown governing equations from observational data.
