Department of Energy Deep Learning
The Department of Energy finances and sponsors a variety of deep learning or machine learning projects. Some of these projects include the use of deep learning to interpret massive scientific data sets, a program aimed at using machine learning to increase the production of biofuel and a new method that can be applied to nuclear reactors to accelerate the speed of predictions and improve accuracy of diagnosis of instability inside the reactor.
1) Interpreting massive data sets
The US Department of Energy (DOE) has financed a $2 million machine learning project with the aim of improving the interpretation of huge scientific data sets. The program is name ASCEND which stands for Advances in Machine Learning to Improve Scientific Discovery at Exascale and Beyond. In this particular project, deep learning is used to help scientists and researchers explore the link that exists between data collected from experiments and properties of various materials. This would ultimately assist researchers in the process of designing improved materials.
The deep learning based technology in this case is a network that is able to make sense of data gathered from a huge number of inputs, and to improve its understanding by learning from the data gathered previously.
2) Increase in Biofuel production
Scientists from a DOE laboratory called the Lawrence Berkeley National Laboratory (LBNL) have used machine learning to assist them in the creation of a new technique to speed the creation of biofuel-producing microbes.
Using machine learning, the researchers have managed to find an algorithm that uses previous experiment data about proteins in a microbe that produces biofuel. Machine learning would then allow to learn how chemical reactions produce a wanted compound, using data from previous experiments.
The use of machine learning would also allow to facilitate the predictive powers of a computer algorithm by allowing it to be trained and to learn from previous operations.
The test of the technique found that the algorithm managed to teach itself about the variations observed in the concentrations of metabolites during the chemical reactions, and to predict with accuracy the amount of biofuel that could be produced.
3) Forecasting instabilities in nuclear reactors
The DOE has sponsored a project called the Fusion Recurrent Neural Network (FRNN) whose aim is to forecast dangerous instabilities in nuclear reactors, that can occur due to a disruption in tokamaks, devices that are used to capture fusion energy.
The FRNN is a new deep learning technique that can be used to accelerate the speed of predictions of potential tokamaks disruptions that can damage nuclear reactors. This breakthrough can have an important impact on nuclear reactors safety in the future, by helping to better understand complex systems.
In this particular case, deep learning has been used to train a computer to solve a complex issue with speed and accuracy, by processing various data until it finds the desired outcome.