An Outlook on Scientific Machine Learning in Continuum Physics

As we pointed out in the preface of this monograph, the topics covered here have been informed by a very specific perspective that the author brings to this subject: that of PDE-based models of continua with applications to the physics of non-biological and biological systems. This led us to study certain data-driven modeling and scientific machine learning methods. This is a very fast-moving field and, even from the same perspective as ours, there have been several exciting developments in the time since this monograph was begun. We touch briefly on some of them in this epilogue of sorts, noting again that the approaches mentioned here have been chosen due to the author’s bias and are presented from his perspective. We do not enter into details, leaving it to the reader to pursue them from some of the cited references.