2 مستودعات
Numerical solvers optimized for parallel execution across graphics hardware.
Distinct from Differential Equation Solvers: Distinct from Differential Equation Solvers: focuses specifically on the GPU-accelerated execution of solvers rather than general-purpose solvers.
Explore 2 awesome GitHub repositories matching scientific & mathematical computing · GPU-Accelerated Solvers. Refine with filters or upvote what's useful.
PhysX is a physics engine SDK designed for calculating real-time rigid body dynamics, fluid simulations, and environmental interactions in virtual applications. It includes a GPU-accelerated physics solver for computing complex particle fluids and combustion models, a voxel fluid simulator for real-time gas, fire, and smoke, and a destruction simulation framework for modeling the fracture of meshes. The SDK features a specialized machine learning physics tensor interface that enables the exchange of simulation data with machine learning frameworks using a common tensor format. It also impleme
Computes fluid dynamics and combustion models using sparse voxel grids processed on parallel graphics hardware.
DifferentialEquations.jl is a comprehensive numerical library designed for solving ordinary, stochastic, delay, and algebraic differential equations. It functions as a high-performance solver suite that integrates scientific machine learning, probabilistic programming, and automated differentiation into a unified framework. By leveraging multiple dispatch and symbolic-numeric integration, the library provides a flexible environment for complex mathematical modeling and simulation. The project distinguishes itself through its ability to bridge traditional numerical analysis with modern machine
Executes numerical solvers in parallel across graphics hardware to accelerate large-scale simulations.