Sparse Modeling and Machine Learning for Nonlinear Partial Differential Equations

Abstract

The objective of this research is to develop, understand, and apply a new approach for discovering the underlying structure in a given dataset generated by an unknown dynamic process.The research strategy will use sparse optimization in order to build learning models. Sparseoptimization has seen many applications in machine learning and image processing, and shouldhave a strong impact in scientific computing and numerical differential equations research.

Document Details

Document Type
DoD Grant Award
Publication Date
May 02, 2017
Source ID
FA95501710125

Entities

People

  • Hayden Schaeffer

Organizations

  • Air Force Office of Scientific Research
  • Massachusetts Institute of Technology
  • United States Air Force

Tags

Readers

  • Distributed Systems and Data Platform Development
  • Finite Element Method (FEM) for solving Partial Differential Equations (PDEs)
  • Neural Network Machine Learning.

Technology Areas

  • AI & ML
  • AI & ML - Machine Learning Algorithms
  • AI & ML - Neural Networks