TorchFSM: Fourier Spectral Method with PyTorch

• Modular by design: TorchFSM offers a modular architecture with essential mathematical operators—like divergence, gradient, and convection—so you can build custom solvers like stacking building blocks, quickly and intuitively.
• GPU-accelerated: TorchFSM leverages GPU computing to speed up simulations dramatically. Run complex 3D PDEs in minutes, not hours, with seamless hardware acceleration.
• Batched simulation support: Built on PyTorch, TorchFSM enables batched simulations with varied initial conditions—ideal for parameter sweeps, uncertainty quantification, or ensemble analysis.
• Differentiable and ML-ready: Fully differentiable by design, TorchFSM integrates naturally with machine learning workflows—for residual operators, differentiable physics, or dataset generation.

For more details, please refer to our homepage.