arXiv:2504.01894v2 Announce Type: replace Abstract: We present a bifidelity method for uncertainty quantification of parameter estimates in complex systems, leveraging generative models trained to sample the target conditional distribution. In the Bayesian inference setting, traditional parameter estimation methods rely on repeated simulations of potentially expensive forward models to determine the posterior distribution of the parameter values, which may result in computationally intractable workflows. Furthermore, methods such as Markov Chain Monte Carlo (MCMC) necessitate rerunning the ent
The increasing computational demands of complex system simulations and the broader AI revolution are driving innovation in more efficient parameter estimation techniques.
This development in conditional diffusion models offers a significant leap in quantifying uncertainty for complex systems, reducing computational costs and accelerating scientific discovery and engineering design.
Traditional computationally intensive Bayesian inference methods for complex systems can now be significantly optimized, leading to faster research cycles and more feasible applications in areas previously constrained by computational limits.
- · AI researchers and developers
- · Scientific computing sector
- · Engineering and design firms
- · Pharmaceutical and materials science
- · High-cost traditional simulation providers
- · Organizations slow to adopt advanced AI methods
Reduced time and cost for sophisticated simulations and uncertainty quantification across various scientific and engineering disciplines.
Accelerated development cycles for new technologies and scientific breakthroughs where complex system modeling is a prerequisite.
Enhanced ability to model and predict behavior in highly complex domains like climate, disease, and financial markets, leading to more robust decision-making.
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Read at arXiv cs.LG