arXiv:2510.10968v3 Announce Type: replace Abstract: Derivative-free Bayesian inversion arises in science and engineering applications, particularly when forward model is costly or infeasible to differentiate through. Existing derivative-free methods collapse the posterior to a point estimate or return severely over-confident uncertainty on high-dimensional, nonlinear problems. We introduce Blade, which produces accurate and well-calibrated posteriors using an ensemble of interacting particles. Blade leverages diffusion models as data-driven priors, and only queries the forward model through fo
The continuous advancements in AI research, particularly in addressing computational limitations and improving model robustness, drive the development of more efficient and accurate methods like Blade.
This development proposes a significant improvement in Bayesian inversion, particularly for complex, high-dimensional problems, which could accelerate progress in scientific and engineering AI applications where traditional methods falter.
The ability to accurately and efficiently perform derivative-free Bayesian inversion with well-calibrated posteriors, especially when forward models are costly or hard to differentiate, fundamentally changes the landscape for certain AI-driven scientific discovery and engineering design processes.
- · Scientific research institutions
- · Engineering design firms
- · AI model developers
- · Industries relying on complex simulations
- · Computational methods relying heavily on derivatives
- · Traditional inversion techniques
Blade's approach could lead to more robust and accurate AI models in fields ranging from materials science to climate modeling.
Reduced computational costs and improved reliability in these fields could accelerate innovation and product development cycles.
Widespread adoption of such techniques might democratize access to advanced simulation and modeling, fostering new areas of scientific inquiry previously limited by computational barriers.
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Read at arXiv cs.LG