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Abstract In robotics, simulation has the potential to reduce design time and costs, and lead to a more robust engineered solution and a safer development process. However, the use of simulators is predicated on the availability of good models. This contribution is concerned with improving the quality of these models via calibration, which is cast herein in a Bayesian framework. First, we discuss the Bayesian machinery involved in model calibration. Then, we demonstrate it in one example: calibration of a vehicle dynamics model that has low degree-of-freedom (DOF) count and can be used for state estimation, model predictive control, or path planning. A high fidelity simulator is used to emulate the “experiments” and generate the data for the calibration. The merit of this work is not tied to a new Bayesian methodology for calibration, but to the demonstration of how the Bayesian machinery can establish connections among models in computational dynamics, even when the data in use is noisy. The software used to generate the results reported herein is available in a public repository for unfettered use and distribution.
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Calibrating Simple Climate Models to Individual Earth System Models: Lessons Learned From Calibrating Hector
Abstract Simple climate models (SCMs) are computationally efficient and capable of emulating global mean output of more complex Earth system models (ESMs). In doing so, SCMs can play a critical role in climate research as stand‐ins for the computationally more expensive models, especially in studies involving low, spatial, and/or temporal resolution, providing more computationally efficient sources of climate data. Here we use Hector v2.5.0 to emulate the multiforcing historical and RCP scenario output for 31 concentration and seven emission‐driven ESMs. When calibrating Hector, sufficient calibration data must be used to constrain the model; otherwise, climate and/or carbon parameters affecting physical processes may be able to trade off with one another, allowing for solutions to use physically unreasonable fitted parameter values as well as limiting the application of the SCM as an emulator. We also present a novel methodology that uses the ESM range as a calibration data, which can be adopted when faced with missing variable output from a specific model.
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- Award ID(s):
- 1931641
- PAR ID:
- 10453618
- Publisher / Repository:
- DOI PREFIX: 10.1029
- Date Published:
- Journal Name:
- Earth and Space Science
- Volume:
- 7
- Issue:
- 11
- ISSN:
- 2333-5084
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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