The essence of discovery in sciences has always been rooted in the reverse engineering of natural phenomena and observational data.  This paradigm of deducing the underlying laws of nature from observable outcomes forms the cornerstone of our scientific inquiry. The DEEP-IN working group is established with the recognition that the elucidation of such complex phenomena demands a fusion of physics insights and advanced deep learning methodologies. 

In response to the evolving landscape of scientific research, our objective is to integrate cutting-edge deep learning techniques, alongside generative models and other advanced statistical learning methods, into the toolkit of scientists. 

The DEEP-IN working group at RIKEN-iTHEMS is dedicated to creating an interdisciplinary platform that harnesses the transformative potential of artificial intelligence(AI). This platform is designed to tackle inverse problems that span a diverse spectrum of sciences, from biology to physics and more in the future.