Nanotechnology Now

Our NanoNews Digest Sponsors
Heifer International



Home > Press > Teaching physics to neural networks removes 'chaos blindness'

The Hamiltonian flow represented as a donut-like torus; rainbow colors code a fourth dimension.
The Hamiltonian flow represented as a donut-like torus; rainbow colors code a fourth dimension.

Abstract:
Researchers from North Carolina State University have discovered that teaching physics to neural networks enables those networks to better adapt to chaos within their environment. The work has implications for improved artificial intelligence (AI) applications ranging from medical diagnostics to automated drone piloting.

Teaching physics to neural networks removes 'chaos blindness'

Raleigh, NC | Posted on June 19th, 2020

Neural networks are an advanced type of AI loosely based on the way that our brains work. Our natural neurons exchange electrical impulses according to the strengths of their connections. Artificial neural networks mimic this behavior by adjusting numerical weights and biases during training sessions to minimize the difference between their actual and desired outputs. For example, a neural network can be trained to identify photos of dogs by sifting through a large number of photos, making a guess about whether the photo is of a dog, seeing how far off it is and then adjusting its weights and biases until they are closer to reality.

The drawback to this neural network training is something called "chaos blindness" - an inability to predict or respond to chaos in a system. Conventional AI is chaos blind. But researchers from NC State's Nonlinear Artificial Intelligence Laboratory (NAIL) have found that incorporating a Hamiltonian function into neural networks better enables them to "see" chaos within a system and adapt accordingly.

Simply put, the Hamiltonian embodies the complete information about a dynamic physical system - the total amount of all the energies present, kinetic and potential. Picture a swinging pendulum, moving back and forth in space over time. Now look at a snapshot of that pendulum. The snapshot cannot tell you where that pendulum is in its arc or where it is going next. Conventional neural networks operate from a snapshot of the pendulum. Neural networks familiar with the Hamiltonian flow understand the entirety of the pendulum's movement - where it is, where it will or could be, and the energies involved in its movement.

In a proof-of-concept project, the NAIL team incorporated Hamiltonian structure into neural networks, then applied them to a known model of stellar and molecular dynamics called the Hénon-Heiles model. The Hamiltonian neural network accurately predicted the dynamics of the system, even as it moved between order and chaos.

"The Hamiltonian is really the 'special sauce' that gives neural networks the ability to learn order and chaos," says John Lindner, visiting researcher at NAIL, professor of physics at The College of Wooster and corresponding author of a paper describing the work. "With the Hamiltonian, the neural network understands underlying dynamics in a way that a conventional network cannot. This is a first step toward physics-savvy neural networks that could help us solve hard problems."

The work appears in Physical Review E and is supported in part by the Office of Naval Research (grant N00014-16-1-3066). NC State postdoctoral researcher Anshul Choudhary is first author. Bill Ditto, professor of physics at NC State, is director of NAIL. Visiting researcher Scott Miller; Sudeshna Sinha, from the Indian Institute of Science Education and Research Mohali; and NC State graduate student Elliott Holliday also contributed to the work.

####

For more information, please click here

Contacts:
Tracey Peake

919-515-6142

@NCStateNews

Copyright © North Carolina State University

If you have a comment, please Contact us.

Issuers of news releases, not 7th Wave, Inc. or Nanotechnology Now, are solely responsible for the accuracy of the content.

Bookmark:
Delicious Digg Newsvine Google Yahoo Reddit Magnoliacom Furl Facebook

Related Links

"Physics enhanced neural networks predict order and chaos"

Related News Press

Physics

INRS and ELI deepen strategic partnership to train the next generation in laser science:PhD students will benefit from international mobility and privileged access to cutting-edge infrastructure June 6th, 2025

Quantum computers simulate fundamental physics: shedding light on the building blocks of nature June 6th, 2025

News and information

INRS and ELI deepen strategic partnership to train the next generation in laser science:PhD students will benefit from international mobility and privileged access to cutting-edge infrastructure June 6th, 2025

Electrifying results shed light on graphene foam as a potential material for lab grown cartilage June 6th, 2025

Quantum computers simulate fundamental physics: shedding light on the building blocks of nature June 6th, 2025

Govt.-Legislation/Regulation/Funding/Policy

INRS and ELI deepen strategic partnership to train the next generation in laser science:PhD students will benefit from international mobility and privileged access to cutting-edge infrastructure June 6th, 2025

Electrifying results shed light on graphene foam as a potential material for lab grown cartilage June 6th, 2025

Institute for Nanoscience hosts annual proposal planning meeting May 16th, 2025

Rice researchers harness gravity to create low-cost device for rapid cell analysis February 28th, 2025

Possible Futures

Ben-Gurion University of the Negev researchers several steps closer to harnessing patient's own T-cells to fight off cancer June 6th, 2025

Researchers unveil a groundbreaking clay-based solution to capture carbon dioxide and combat climate change June 6th, 2025

Cambridge chemists discover simple way to build bigger molecules – one carbon at a time June 6th, 2025

A 1960s idea inspires NBI researchers to study hitherto inaccessible quantum states June 6th, 2025

Discoveries

Researchers unveil a groundbreaking clay-based solution to capture carbon dioxide and combat climate change June 6th, 2025

Cambridge chemists discover simple way to build bigger molecules – one carbon at a time June 6th, 2025

Electrifying results shed light on graphene foam as a potential material for lab grown cartilage June 6th, 2025

A 1960s idea inspires NBI researchers to study hitherto inaccessible quantum states June 6th, 2025

Announcements

INRS and ELI deepen strategic partnership to train the next generation in laser science:PhD students will benefit from international mobility and privileged access to cutting-edge infrastructure June 6th, 2025

Electrifying results shed light on graphene foam as a potential material for lab grown cartilage June 6th, 2025

Quantum computers simulate fundamental physics: shedding light on the building blocks of nature June 6th, 2025

A 1960s idea inspires NBI researchers to study hitherto inaccessible quantum states June 6th, 2025

Interviews/Book Reviews/Essays/Reports/Podcasts/Journals/White papers/Posters

Cambridge chemists discover simple way to build bigger molecules – one carbon at a time June 6th, 2025

Electrifying results shed light on graphene foam as a potential material for lab grown cartilage June 6th, 2025

Quantum computers simulate fundamental physics: shedding light on the building blocks of nature June 6th, 2025

A 1960s idea inspires NBI researchers to study hitherto inaccessible quantum states June 6th, 2025

Military

Quantum engineers ‘squeeze’ laser frequency combs to make more sensitive gas sensors January 17th, 2025

Chainmail-like material could be the future of armor: First 2D mechanically interlocked polymer exhibits exceptional flexibility and strength January 17th, 2025

Single atoms show their true color July 5th, 2024

NRL charters Navy’s quantum inertial navigation path to reduce drift April 5th, 2024

Artificial Intelligence

Autonomous AI assistant to build nanostructures: An interdisciplinary research group at TU Graz is working on constructing logic circuits through the targeted arrangement of individual molecules: Artificial intelligence should speed up the process enormously January 17th, 2025

New quantum encoding methods slash circuit complexity in machine learning November 8th, 2024

Rice research could make weird AI images a thing of the past: New diffusion model approach solves the aspect ratio problem September 13th, 2024

Simulating magnetization in a Heisenberg quantum spin chain April 5th, 2024

NanoNews-Digest
The latest news from around the world, FREE




  Premium Products
NanoNews-Custom
Only the news you want to read!
 Learn More
NanoStrategies
Full-service, expert consulting
 Learn More











ASP
Nanotechnology Now Featured Books




NNN

The Hunger Project