Signal propagation in complex networks drives epidemics, is responsible for information
going viral, promotes trust and facilitates moral behavior in social groups, enables the …

The field of dynamical systems is being transformed by the mathematical tools and
algorithms emerging from modern computing and data science. First-principles derivations …

There is a growing consensus that solutions to complex science and engineering problems
require novel methodologies that are able to integrate traditional physics-based modeling …

There is a growing consensus that solutions to complex science and engineering problems
require novel methodologies that are able to integrate traditional physics-based modeling …

Incorporating symmetry as an inductive bias into neural network architecture has led to
improvements in generalization, data efficiency, and physical consistency in dynamics …

Real-world time series are characterized by intrinsic non-stationarity that poses a principal
challenge for deep forecasting models. While previous models suffer from complicated …

Physics-informed machine learning (PIML), referring to the combination of prior knowledge
of physics, which is the high level abstraction of natural phenomenons and human …

Abstract The use of Machine Learning (ML) has rapidly spread across several fields of
applied sciences, having encountered many applications in Structural Dynamics and …

In many applications, it is important to reconstruct a fluid flow field, or some other high-
dimensional state, from limited measurements and limited data. In this work, we propose a …

Vast amount of data generated from networks of sensors, wearables, and the Internet of
Things (IoT) devices underscores the need for advanced modeling techniques that leverage …