Scientific Achievement
A Foundry scientist trained neural networks to operate simulated fluctuating nanosystems in an energy-efficient way, converting information into heat or work.
Significance and Impact
The approach could be adapted for use with physical computing devices, in order to reduce energy consumption and waste.
Research Details
- Used genetic algorithms to train neural-network feedback-control protocols for simulated fluctuating nanosystems
- Demonstrated procedure on model systems of a particle pulled through a viscous medium by an optical trap and an Ising model undergoing magnetization reversal
- When provided with feedback from the system, neural networks learned protocols to extract work or store heat
