Emergence of complex life would be impossible without cooperation between biological entities such as genes, cells, or organisms. Yet, Darwinian evolution appears to promote self-centered ...

Abstract: Dynamic Graph Neural Networks (GNNs) combine temporal information with GNNs to capture structural, temporal, and contextual relationships in dynamic graphs simultaneously, leading to ...

This work introduces a model-agnostic framework for training and inference to enable accurate partial differential equation solving (down to double precision) for problems with arbitrary sizes and ...

The PyCX project aims to develop an online repository of simple, crude, yet easy-to-understand Python sample codes for dynamic complex systems modeling and simulation, including iterative maps, ...

Creative Commons (CC): This is a Creative Commons license. Attribution (BY): Credit must be given to the creator. Programming is a key transferable skill within the chemical sciences with applications ...

Solving complex Partial Differential Equations (PDEs) accurately and efficiently is an essential and challenging problem in all scientific and engineering disciplines. Mesh movement methods provide ...

If you’re fed up with being handed decade-old battered calculators and struggling to read the numbers on the faded screens, it might be time to invest in a calculator of your own. Aside from being ...

Machine learning is a complex discipline but implementing machine learning models is far less daunting than it used to be. Machine learning frameworks like Google’s TensorFlow ease the process of ...

We report an educational tool for the upper level undergraduate quantum chemistry or quantum physics course that uses a symbolic approach via the PySyComp Python library. The tool covers both ...