CMX Lunch Seminar

Machine learning is emerging as an essential tool in many science and engineering domains, fueled by extraordinarily powerful computers as well as advanced instruments capable of collecting high-resolution and high-dimensional experimental data. However, using off-the-shelf machine learning methods for analyzing scientific and engineering data fails to leverage our vast, collective (albeit partial) understanding of the underlying physical phenomenon or models of sensor systems. Reconstructing physical phenomena from indirect scientific observations is at the heart of scientific measurement and discovery, and so a pervasive challenge is to develop new methodologies capable of combining such physical models with training data to yield more rapid, accurate inferences. We will explore these ideas in the context of inverse problems and data assimilation challenges; examples include climate forecasting, uncovering material structure and properties, and medical image reconstruction. Classical approaches to such inverse problems and data assimilation approaches have relied upon insights from optimization, signal processing, and the careful exploitation of forward models. In this talk, we will see how these insights and tools can be integrated into machine learning systems to yield novel methods with significant accuracy and computational advantages over naïve applications of machine learning.