Scott Thomas Miller

Title: Scott Thomas Miller: Innovator in Neural Networks

Introduction

Scott Thomas Miller is an accomplished inventor based in Brooklyn, NY. He has made significant contributions to the field of neural networks, particularly in the context of dynamical systems. His innovative work focuses on the intersection of order and chaos, providing valuable insights into complex systems.

Latest Patents

Scott Thomas Miller holds a patent for "Physics augmented neural networks configured for operating in environments that mix order and chaos." This patent outlines methods, systems, and computer-readable media for utilizing an augmented neural network. The method includes utilizing a neural network pre-processor to convert generic coordinates associated with a dynamical system to canonical coordinates. It also involves concatenating a Hamiltonian neural network to the pre-processor to create a generalized Hamiltonian neural network. The trained generalized Hamiltonian neural network is then used to forecast nonlinear dynamics and quantify chaotic behavior, ultimately discovering transitions between orderly and chaotic states.

Career Highlights

Scott is affiliated with North Carolina State University, where he continues to advance his research in neural networks and dynamical systems. His work has garnered attention for its innovative approach to understanding complex behaviors in various environments.

Collaborations

Scott has collaborated with notable colleagues, including William Lawrence Ditto and John Florian Lindner. Their combined expertise contributes to the advancement of research in neural networks and their applications.

Conclusion

Scott Thomas Miller is a pioneering inventor whose work in augmented neural networks is shaping the future of understanding dynamical systems. His contributions are essential for exploring the complexities of order and chaos in various fields.