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Active Subspace Techniques to Construct Surrogate Models for Complex Physical and Biological Models

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UNQW02 - Surrogate models for UQ in complex systems

For many complex physical and biological models, the computational cost of high-fidelity simulation codes precludes their direct use for Bayesian model calibration and uncertainty propagation. For example, the considered neutronics and nuclear thermal hydraulics codes can take hours to days for a single run. Furthermore, the models often have tens to thousands of inputs—comprised of parameters, initial conditions, or boundary conditions—many of which are unidentifiable in the sense that they cannot be uniquely determined using measured responses. In this presentation, we will discuss techniques to isolate influential inputs for subsequent surrogate model construction for Bayesian inference and uncertainty propagation. For input selection, we will discuss advantages and shortcomings of global sensitivity analysis to isolate influential inputs and the use of active subspace construction to determine low-dimensional input manifolds. We will also discuss the manner in which Bayesian calibration on active subspaces can be used to quantify uncertainties in physical parameters. These techniques will be illustrated for models arising in nuclear power plant design, quantum-informed material characterization, and HIV modeling and treatment.

This talk is part of the Isaac Newton Institute Seminar Series series.

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