Machine Learning, for Science

Increasingly available data and rising computational power have combined to usher in a new age of information. We seldom go a day without using some service powered by sophisticated techniques from the data sciences.
Machine learning is a set of techniques that has revolutionized the modern world. These approaches involve computer programs that analyze features in input data and develop their own ways of identifying relevant patterns and information. Its applications range from voice recognition in our cell phones and cars to internet searches and recommendation systems. However, scientists have only begun to tailor machine learning for effective use in scientific research.
To address these challenges, the U.S. Department of Energy has awarded a collaborative grant to a group of researchers, including UC Santa Barbara mathematician Paul Atzberger, to establish a new data science research center. The Physics-Informed Learning Machines for Multiscale and Multiphysics Problems — also known as the PhILMs MMICCs center — will innovate on existing machine learning techniques and develop new ones that are better adapted to problems in the sciences and engineering. The grant will provide $600,000 over four years to support Atzberger’s research group at UC Santa Barbara.
Paul Atzberger
  • grant
  • data science
  • machine learning