ConDiSim: Conditional Diffusion Models for Simulation-Based Inference

We present ConDiSim, a conditional diffusion model for simulation-based inference in 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 provides a robust and extensible framework for simulation-based inference, well suited to parameter estimation tasks that demand fast methods for handling noisy, time series observations.