Statistics for topic computational-physics
RepositoryStats tracks 595,858 Github repositories, of these 39 are tagged with the computational-physics topic. The most common primary language for repositories using this topic is C++ (13). Other languages include: Python (13)
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Geant4 toolkit for the simulation of the passage of particles through matter - NIM A 506 (2003) 250-303
Geant4 toolkit for the simulation of the passage of particles through matter - NIM A 506 (2003) 250-303
Geant4 toolkit for the simulation of the passage of particles through matter - NIM A 506 (2003) 250-303
[ICLR 2024] EquiformerV2: Improved Equivariant Transformer for Scaling to Higher-Degree Representations
Geant4 toolkit for the simulation of the passage of particles through matter - NIM A 506 (2003) 250-303
[ICLR 2024] EquiformerV2: Improved Equivariant Transformer for Scaling to Higher-Degree Representations
Fluid simulation engine for computer graphics applications
Geant4 toolkit for the simulation of the passage of particles through matter - NIM A 506 (2003) 250-303
[ICLR 2024] EquiformerV2: Improved Equivariant Transformer for Scaling to Higher-Degree Representations
Lecture notes and code for the course PHYS6350 Computational Physics at the University of Houston
An interoperable Python framework for biomolecular simulation.
Hydrogen wavefunction modeling and electron probability density plots
Celeritas is a new Monte Carlo transport code designed to accelerate scientific discovery in high energy physics by improving detector simulation throughput and energy efficiency using GPUs.
Fluid simulation engine for computer graphics applications
Geant4 toolkit for the simulation of the passage of particles through matter - NIM A 506 (2003) 250-303
[ICLR 2024] EquiformerV2: Improved Equivariant Transformer for Scaling to Higher-Degree Representations
Hydrogen wavefunction modeling and electron probability density plots
[ICLR 2024] EquiformerV2: Improved Equivariant Transformer for Scaling to Higher-Degree Representations
An interoperable Python framework for biomolecular simulation.
Using Physics-Informed Deep Learning (PIDL) techniques (W-PINNs-DE & W-PINNs) to solve forward and inverse hydrodynamic shock-tube problems and plane stress linear elasticity boundary value problems