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On deep learning in physics
Machine learning, and most notably deep neural networks, have seen unprecedented success in recent years due to their ability to learn complex nonlinear mappings by ingesting large amounts of data through the process of ...
Opportunities for the deep neural network method of solving partial differential equations in the computational study of biomolecules driven through periodic geometries
As deep learning emerged in the 2010s to become a groundbreaking technology in machine vision and natural language processing, it also ushered in many new algorithms for use in scientific research. Among these is the neural ...
Molecular dynamics simulations and neural network solutions for applications in biophysics
As computing resources evolved and became more accessible over time, much of scientific research shifted towards utilizing computational techniques. In particular, biophysics is a field of science that has continually ...