MUMS Working Group III: Prediction Uncertainty and Extrapolation

Group Leaders:
David Higdon (Virginia Tech)
Pierre Barbillon (Agro Paris Tech)

Pulong Ma

This research thrust seeks to develop methods and case studies for developing physically motivated error models to account for the discrepancy between computational model and reality, resulting in more reliable prediction uncertainties. This thrust will consider a number of applications and activities that will motivate new modeling approaches, helping to determine overarching principles for identifying settings in which reliable prediction uncertainties can be developed.

Potential projects include:

  • discrepancy models for OCO-2 or simpler test model
  • discovering pde structure to better characterize discrepancy between model and reality
  • use of stochastic “emulators” to better capture the link between model and reality
  • Discrepancy models in rich/structured design space
  • Designing model runs and physical experiments in additive manufacturing
  • Potential workshop on agent-based models in November

Questions: email

Program on Model Uncertainty: Mathematical and Statistical (MUMS)