More than 20% of the workload on the world's 500 fastest supercomputers is spent simulating how atoms and molecules move—with applications ranging from material design to identifying drug interactions ...

This is a Python code for timeseries analysis using WWZ transformations. It uses Foster's abbreviated Morlet Wavelet to analyse timeseries using a WWZ (Weighted ...

This report serves as an exhaustive, practical guide for building a complete industrial data acquisition and testing application using Python. Moving beyond basic scripting, we will introduce ...

The ash yield resulting from the alteration of inorganic elements during the processes of combustion and gasification of coal stands as a crucial quality indicator for coal. Ash yield, along with ...

Operator learning is a transformative approach in scientific computing. It focuses on developing models that map functions to other functions, an essential aspect of solving partial differential ...

Vibroseis Similarity tests for controlled-source land seismic surveys are routinely performed to assess the output performance of vibrator mechanics. These tests are crucial to make sure the output ...

S2FFT is a Python package for computing Fourier transforms on the sphere and rotation group (Price & McEwen 2024) using JAX or PyTorch. It leverages autodiff to provide differentiable transforms, ...

Department of Computer Science, University of York, York, UK. Kolmogorov complexity is a unique formalisation of complexity which quantifies “information” from an algorithmic point of view [1] and has ...

The spectral analysis of signals is currently either dominated by the speed–accuracy trade-off or ignores a signal’s often non-stationary character. Here we introduce an open-source algorithm to ...

Physics-Informed Neural Networks (PINN) are neural networks encoding the problem governing equations, such as Partial Differential Equations (PDE), as a part of the neural network. PINNs have emerged ...