Graph Neural Networks (GNNs) with equivariant properties have emerged as powerful tools
for modeling complex dynamics of multi-object physical systems. However, their …

This study introduces a novel parameter estimation method based on Neural Ordinary
Differential Equations (Neural ODE). The method addresses the challenges of limited data …

The process of inference on networks of spiking neurons is essential to decipher the
underlying mechanisms of brain computation and function. In this study, we conduct …

In this article, we leverage standard-compliant beam training measurements from
commercial millimeter-wave (mmWave) Wi-Fi communication devices for object localization …

Data-driven surrogate modeling has emerged as a promising approach for reducing
computational expenses of multi-scale simulations. Recurrent Neural Network (RNN) is a …

Experimental data is often comprised of variables measured independently, at different
sampling rates (non-uniform Δ t between successive measurements); and at a specific time …

We focus on learning unknown dynamics from data using ODE-nets templated on implicit
numerical initial value problem solvers. First, we perform inverse modified error analysis of …

Many physical systems can be described as a continuous-time dynamical system. In
practice, the true system is often unknown and has to be learned from measurement data …

Data-driven modeling and machine learning are widely used to model the behavior of
dynamic systems. One application is the residual evaluation of technical systems where …

The process of making inference on networks of spiking neurons is crucial to decipher the
underlying mechanisms of neural computation. Mean-field theory simplifies the interactions …