We present the pyerrors python package for statistical error analysis of Monte Carlo data.
High Energy Physics - Lattice Computational Physics Data Analysis, Statistics and Probability
Neural operators can learn nonlinear mappings between function spaces and offer a new simulation paradigm for real-time prediction of complex dynamics for realistic diverse applications as well as for system identification in science and engineering.
Amorphous silicon (a-Si) is an important thermal-management material and also serves as an ideal playground for studying heat transport in strongly disordered materials.
Materials Science Computational Physics
Classical force fields (FF) based on machine learning (ML) methods show great potential for large scale simulations of materials.
Python is a free, easy to use a high-level programming language which has seen a huge expansion in the number of its users and developers in recent years.
General Relativity and Quantum Cosmology Cosmology and Nongalactic Astrophysics Instrumentation and Methods for Astrophysics 83-04
The quantum approximate optimization algorithm (QAOA) is widely seen as a possible usage of noisy intermediate-scale quantum (NISQ) devices.
The novel system is a non-dispersive non-hydrostatic extension of the classical Saint-Venant equations.
Classical Physics Analysis of PDEs Numerical Analysis Computational Physics Fluid Dynamics
In this paper the basic functionality of PyPSA is described, including the formulation of the full power flow equations and the multi-period optimisation of operation and investment with linear power flow equations.
Physics and Society
We perform a data-driven dimensionality reduction of the scale-dependent 4-point vertex function characterizing the functional Renormalization Group (fRG) flow for the widely studied two-dimensional $t - t'$ Hubbard model on the square lattice.
Strongly Correlated Electrons Disordered Systems and Neural Networks