ConDiSim: Conditional Diffusion Models for Simulation Based Inference

We present a conditional diffusion model - ConDiSim, for simulation-based inference of complex systems with intractable likelihoods. ConDiSim leverages denoising diffusion probabilistic models to approximate posterior distributions, consisting of a forward process that adds Gaussian noise to parameters, and a reverse process learning to denoise, conditioned on observed data. This approach effectively captures complex dependencies and multi-modalities within posteriors. ConDiSim is evaluated across ten benchmark problems and two real-world test problems, where it demonstrates effective posterior approximation accuracy while maintaining computational efficiency and stability in model training. ConDiSim offers a robust and extensible framework for simulation-based inference, particularly suitable for parameter inference workflows requiring fast inference methods.

@article{nautiyal2025_2505.08403,
  title={ ConDiSim: Conditional Diffusion Models for Simulation Based Inference },
  author={ Mayank Nautiyal and Andreas Hellander and Prashant Singh },
  journal={arXiv preprint arXiv:2505.08403},
  year={ 2025 }
}