Position: Optimization in SciML Should Employ the Function Space Geometry

Abstract

Scientific machine learning (SciML) is a relatively new field that aims to solve problems from different fields of natural sciences using machine learning tools. It is well-documented that the optimizers commonly used in other areas of machine learning perform poorly on many SciML problems. We provide an infinite-dimensional view on optimization problems encountered in scientific machine learning and advocate for the paradigm first optimize, then discretize for their solution. This amounts to first choosing an appropriate infinite-dimensional algorithm which is then discretized in a second step. To illustrate this point, we show that recently proposed state-of-the-art algorithms for SciML applications can be derived within this framework. As the infinite-dimensional viewpoint is presently underdeveloped in scientific machine learning, we formalize it here and advocate for its use in SciML in the development of efficient optimization algorithms.