arXiv:2605.27499v1 Announce Type: new Abstract: Flow and diffusion generative models have established themselves as widely adopted density estimators for simulation-based inference (SBI), extending naturally from neural posterior estimation to likelihood and joint density estimation. Their principled optimization objectives and freedom from architectural constraints have driven rapid adoption across the natural sciences. Yet the most widely used SBI libraries remain PyTorch-based, leaving researchers who develop their forward models and analysis pipelines in JAX without a native option. We pre
The proliferation of advanced generative models combined with the increasing adoption of JAX in scientific computing creates a need for native integration in simulation-based inference workflows.
This development addresses a critical tooling gap for researchers using JAX, potentially accelerating scientific discovery and AI model development in computationally intensive fields like astrophysics.
Scientists and machine learning researchers leveraging JAX for their forward models will now have a native, potentially more efficient, option for generative simulation-based inference, reducing reliance on PyTorch-centric libraries.
- · JAX ecosystem
- · Generative AI researchers
- · Astrophysics community
- · Scientific computing
- · PyTorch-exclusive SBI libraries
Increased adoption and efficiency of generative SBI within JAX-based research pipelines.
Faster exploration of complex scientific models and hypotheses due to improved inference tools.
Potential for new scientific discoveries or AI applications enabled by the unique strengths of JAX and advanced SBI methods.
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Read at arXiv cs.LG