Social media has become an important communication channel during high impact events, such as the COVID-19 pandemic.
In the space of only a few years, deep generative modeling has revolutionized how we think of artificial creativity, yielding autonomous systems which produce original images, music, and text.
The number of scientific journal articles and reports being published about energetic materials every year is growing exponentially, and therefore extracting relevant information and actionable insights from the latest research is becoming a considerable challenge.
Due to its high computational speed and accuracy compared to ab-initio quantum chemistry and forcefield modeling, the prediction of molecular properties using machine learning has received great attention in the fields of materials design and drug discovery.
In this work, we discuss use of machine learning techniques for rapid prediction of detonation properties including explosive energy, detonation velocity, and detonation pressure.
Materials Science Chemical Physics Computational Physics
In this work, we first introduce a flexible ICA algorithm that uses an effective PDF estimator to accurately capture the underlying statistical properties of the data.
ICA algorithms cast in the ML framework often deviate from its theoretical optimality properties due to poor estimation of the source PDF.
Independent component analysis (ICA) is a powerful method for blind source separation based on the assumption that sources are statistically independent.