DNAmf
0.1.1Diffusion 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
- OK2026-08-0513 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
- NOTE2026-08-0112 OK · 1 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
- OK2026-06-0913 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
- ERROR2026-06-0812 OK · 0 NOTE · 0 WARNING · 1 ERROR · 0 FAILURE
- OK2026-03-1014 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
Documentation
- Examples that run
- 100%
- Documented parameters
- 95%
- Return-value docs
- 100%
- References docs
- 0%
Downloads
Dependencies
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Code & Tests
People & History
2 releases. Pick two to compare their code metrics. R releases are shown for context.
- 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
- Installed size
- not tracked yet
- With dependencies
- not tracked yet
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