We’re happy to announce version 2.4 of PhiFlow, our differentiable simulation framework for machine learning. Among others, it now has improved support for sparse matrices, preconditioners and plotting: https://github.com/tum-pbs/PhiFlow/pull/116
The new features include:
- Improved plots with additional recipes for bar charts and error bars.
- Improved learning curve visualization with vis.load_scalars()
- Decorator @math.broadcast to make functions compatible with Tensors.
- Preconditioned linear solves (experimental) with ilu and cluster preconditioners
- Improved support for sparse matrices
- Additional math functions, such as soft_plus, factorial, log_gamma, safe_div, primal, is_inf, is_nan
- Explicit device management with math.to_device()
- Tensor unstacking to dict using **tensor.dim
- Jit-compilable sparse neighbor search
- Improved support for Φ-trees, added math.slice().
- Broadcast string formatter using -f-f”…”
