We propose a method for training ordinary differential equations by using a control-theoretic
Lyapunov condition for stability. Our approach, called LyaNet, is based on a novel Lyapunov …

Physics-informed machine learning (PIML) is a set of methods and tools that systematically
integrate machine learning (ML) algorithms with physical constraints and abstract …

Dynamics model learning deals with the task of inferring unknown dynamics from
measurement data and predicting the future behavior of the system. A typical approach to …

Learning stable dynamics from observed time-series data is an essential problem in
robotics, physical modeling, and systems biology. Many of these dynamics are represented …

In order to stabilize nonlinear systems modeled by stochastic differential equations, we
design a Fast Exponentially Stable and Safe Neural Controller (FESSNC) for fast learning …

Motion control of modern and advanced aircraft has to be provided under conditions of
incomplete and inaccurate knowledge of their parameters and characteristics, possible flight …

Autoregressive models are ubiquitous tools for the analysis of time series in many domains
such as computational neuroscience and biomedical engineering. In these domains, data is …

Neural ordinary differential equations (ODEs)(Neural ODEs) construct the continuous
dynamics of hidden units using ODEs specified by a neural network, demonstrating …

Deep equilibrium (DEQ) models have emerged as a promising class of implicit layer models
in deep learning, which abandon traditional depth by solving for the fixed points of a single …

Macroscopic observables of a system are of keen interest in real applications such as the
design of novel materials. Current methods rely on microscopic trajectory simulations, where …