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| Name | IACS Seminar Speaker: Nathan Urban, BNL, February 10th 2022 |
| Video Uploader | Video From IACS Stony Brook |
| Upload Date | This Video Uploaded At 30-03-2022 20:26:18 |
| Video Discription | Model structural uncertainty quantification Abstract: Computer models based on physical equations are often used to make predictions of complex systems. A common source of uncertainty in model predictions is parametric uncertainty, arising when models contain tuning parameters whose values are not perfectly known. A more general, and difficult, source of uncertainty is when the model equations themselves are not perfectly known, leading to model “structural” uncertainty or model-form error. This arises when modelers make choices as to how to approximate the true system, giving different possible models. I will present, at a high-level, various approaches for handling model structural uncertainty, in different settings: (1) multi-model uncertainty across a collection of commensurate “peer” models; (2) uncertainty in a heterogeneous collection of models (e.g. a computational workflow composed of different sub-models); (3) uncertainty in a single model when the model code can be modified (“intrusive”); (4) uncertainty in a single model when the model code cannot be modified (“non-intrusive” / “black-box”). The methods discussed include Bayesian hierarchical modeling for multi-model combination or model blending, probabilistic graphical models (Bayesian networks) of surrogate models or emulators, machine learning for system identification and projective model order reduction, and differentiable programming for online learning of hybrid physical/data-driven models. [aKdkftuTfFY] |
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