Trending Research
A FFT-based finite-difference solver for massively-parallel direct numerical simulations of turbulent flows
We present an efficient solver for massively-parallel direct numerical simulations of incompressible turbulent flows.
Fluid Dynamics
Expressing and Analyzing Quantum Algorithms with Qualtran
Quantum computing's transition from theory to reality has spurred the need for novel software tools to manage the increasing complexity, sophistication, toil, and fallibility of quantum algorithm development.
Quantum Physics Programming Languages
Beyond full statevector simulation with Qibo
In this proceedings, we present two new quantum circuit simulation protocols recently added as optional backends to Qibo, an open-source framework for quantum simulation, hardware control and calibration.
Quantum Physics Computational Physics
Acceleration without Disruption: DFT Software as a Service
Density functional theory (DFT) has been a cornerstone in computational chemistry, physics, and materials science for decades, benefiting from advancements in computational power and theoretical methods.
Chemical Physics
Hypothesis Learning in Automated Experiment: Application to Combinatorial Materials Libraries
Machine learning is rapidly becoming an integral part of experimental physical discovery via automated and high-throughput synthesis, and active experiments in scattering and electron/probe microscopy.
Materials Science Data Analysis, Statistics and Probability
Advanced modeling of materials with PAOFLOW 2.0: New features and software design
Moreover, we have included models for non-constant relaxation time in electronic transport calculations, doubling the real space dimensions of the Hamiltonian as well as the construction of Hamiltonians directly from analytical models.
Materials Science
Geodesic intersections
A complete treatment of the intersections of two geodesics on the surface of an ellipsoid of revolution is given.
Geophysics
Differentiable programming for particle physics simulations
grinisrit/noa
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We describe how to apply adjoint sensitivity methods to backward Monte-Carlo schemes arising from simulations of particles passing through matter.
Computational Physics Data Analysis, Statistics and Probability
A comprehensive and fair comparison of two neural operators (with practical extensions) based on FAIR data
lululxvi/deepxde
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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.
Computational Physics
Deep-Learning Density Functional Theory Hamiltonian for Efficient ab initio Electronic-Structure Calculation
mzjb/deeph-pack
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The marriage of density functional theory (DFT) and deep learning methods has the potential to revolutionize modern computational materials science.
Materials Science Disordered Systems and Neural Networks Mesoscale and Nanoscale Physics Computational Physics Quantum Physics
