Artificial intelligence and mechanical engineering are two mature fields of science that
intersect more and more often. Computer-aided mechanical analysis tools, including …

Effective inclusion of physics-based knowledge into deep neural network models of
dynamical systems can greatly improve data efficiency and generalization. Such a priori …

Complex physical systems are often described by partial differential equations (PDEs) that
depend on parameters such as the Reynolds number in fluid mechanics. In applications …

In this work, we consider the task of improving the accuracy of dynamic models for model
predictive control (MPC) in an online setting. Although prediction models can be learned …

Graph neural networks (GNNs), in general, are built on the assumption of a static set of
features characterizing each node in a graph. This assumption is often violated in practice …

We describe a framework that can integrate prior physical information, eg, the presence of
kinematic constraints, to support data-driven simulation in multibody dynamics. Unlike other …

The principle of least action is one of the most fundamental physical principle. It says that
among all possible motions connecting two points in a phase space, the system will exhibit …

The article shows how to learn models of dynamical systems from data which are governed
by an unknown variational PDE. Rather than employing reduction techniques, we learn a …

Machine learning has become increasingly popular for efficiently modelling the dynamics of
complex physical systems, demonstrating a capability to learn effective models for dynamics …

Mechanical meta-materials are solids whose geometric structure results in exotic nonlinear
behaviors that are not typically achievable via homogeneous materials. We show how to …