Diffusion Non-Additive Model with Tunable Precision
Overview
Performs Diffusion Non-Additive (DNA) model proposed by Heo, Boutelet, and Sung (2025+) doi:10.48550/arXiv.2506.08328 for multi-fidelity computer experiments with tuning parameters. The DNA model captures nonlinear dependencies across fidelity levels using Gaussian process priors and is particularly effective when simulations at different fidelity levels are nonlinearly correlated. The DNA model targets not only interpolation across given fidelity levels but also extrapolation to smaller tuning parameters including the exact solution corresponding to a zero-valued tuning parameter, leveraging a nonseparable covariance kernel structure that models interactions between the tuning parameter and input variables. Closed-form expressions for the predictive mean and variance enable efficient inference and uncertainty quantification. Hyperparameters in the model are estimated via maximum likelihood estimation.
Install
Health
- OK13 OK2026-08-05
- NOTE12 OK, 1 NOTE2026-08-01
- OK13 OK2026-06-09
- ERROR12 OK, 1 ERROR2026-06-08
- OK14 OK2026-03-10
Documentation
- Help pages
- 4
- Examples that run
- 100%2 run in a plain check
- Exported functions with examples
- 50%
- Documented parameters
- 95%
- Return-value docs
- 100%
- Help pages citing references
- 0%
Downloads
Dependencies
Code & Tests
People & History
Pick two releases to compare their code metrics.
- RR 4.6.0 released · 2026-04-24
- 0.1.1Latest
- 0.1.02025-06-23
- RR 4.5.0 released · 2025-04-11
Package metadata
- First published
- 2025-06-23
- Total releases
- 2 / 1 yrs
- License
- GPL-3 OSI
- Download size
- 29 KB
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