Opportunities and Limitations for Deep Learning in the Sciences

Philipp Grohs examines the potential and constraints of utilizing deep learning methods in computational sciences. This approach, integrating modern ML tools, shows promise as an alternative to traditional numerical schemes in various domains such as fluid dynamics, computational finance, and computational chemistry.