Physics-Informed Neural Networks to Model and Control Robots: A Theoretical and Experimental Investigation

This work concerns the application of physics-informed neural networks to the modeling and control of complex robotic systems. Achieving this goal requires extending physics-informed neural networks to handle nonconservative effects. These learned models are proposed to combine with model-based controllers originally developed with first...

Physics-informed Data Augmentation for Human Radar Signatures

In recent years, neural networks (NNs) have seen a surge in popularity due to their ability to model complex patterns and relationships in data. One of the challenges of using NNs is the requirement for large amounts of labelled data to train the model effectively. In many real-world applications such as radar, labelled data may be scarce due to...

Application of Deep Neural Networks to the Operator Space of Nonlinear PDE for Physics-based Proxy Modeling

Compositional simulation is computationally intensive for high-fidelity models due to thermodynamic equilibrium relations and the coupling of flow, transport and mass transfer. In this report, two methods for accelerated compositional simulation are outlined and demonstrated for a gas vaporization problem. The first method uses a proxy model...

Velocity Reconstruction in Pool Fires using Physics-Informed Machine Learning

In many flow experiments it is complex to measure all flow states of interest, leading to the need for a method to retrieve unmeasured flow states from measured ones. This work focuses on Hidden Fluid Mechanics (HFM), which refers to a Physics-Informed Neural Network (PINN) able to incorporate the Navier-Stokes (NS) equations into the loss...

Solving Partial Differential Equations using Physics-Informed Neural Networks

In an attempt to find alternatives for solving partial differential equations (PDEs)<br/>with traditional numerical methods, a new field has emerged which incorporates<br/>the residual of a PDE into the loss function of an Artificial Neural Network. This<br/>method is called Physics-Informed Neural Network (PINN). In this thesis, we study dense...

Predicting traction return current in electric railway systems through physics-informed neural networks

This paper addresses the problem of determining the distribution of the return current in electric railway traction systems. The dynamics of traction return current are simulated in all three space dimensions by informing the neural networks with the Partial Differential Equations (PDEs) known as telegraph equations. In addition, this work...

PINN inspired Freeform Design: Using Fraunhofer Diffraction to find Freeforms described by B-spline Surfaces

This project aims to recreate intensity patterns using Fraunhofer diffraction as a means of simulation. These intensity patterns are created by phase shifting specific parts of an incoming field of light. These phase shifts are determined by a B-spline surface, which is in turn controlled by so-called control points. Only a handful of control...