Xun (Ryan) Huan is an Assistant Professor of Mechanical Engineering at the University of Michigan, and affiliated faculty to the Michigan Institute for Computational Discovery & Engineering (MICDE), the Michigan Institute for Data Science (MIDAS), and the U-M Applied Physics Program. Prior to U-M, he was a postdoctoral researcher at Sandia National Laboratories in Livermore, California. His research broadly revolves around uncertainty quantification, machine learning, and numerical optimization. He is particularly interested in optimal experimental design, Bayesian analysis, multilevel and multifidelity methods, model misspecification, and scientific computing. Outside work, Xun is passionate about aviation and holds a private pilot certificate.
Ph.D. in Computational Science and Engineering, 2015
Massachusetts Institute of Technology
S.M. in Aeronautics and Astronautics, 2010
Massachusetts Institute of Technology
B.A.Sc. in Engineering Science (Aerospace), 2008
University of Toronto
(2019/08) Xun Huan is giving a presentation “Uncertainty Quantification via Optimal Experimental Design and Bayesian Neural Networks for Aerospace Applications” at the National Institute of Aerospace Computational Fluid Dynamics Seminar.
(2019/07) Xun Huan gave a presentation “Sequential Optimal Experimental Design via Reinforcement Learning” at the Workshop on Machine Learning and Uncertainty Quantification.
Experiments are often expensive, time-consuming, and perhaps even dangerous. However, not all experiments are equal, and some can produce more valuable data than others. Identifying and executing these “good” experiments can lead to substantial resource savings.
We develop mathematical frameworks and computational algorithms for optimal experimental design (OED), and seek to address questions such as:
Where and when should we take measurements?
Have we gathered enough data and should stop, or continue with more experiments?
OED is a form of decision-making under uncertainty. For example, when our experimental goal is to learn about model parameters from noisy measurements, a possible approach involves finding the design decision maximizing the expected information gain (mutual information).
There are many challenges associated with finding the optimal design, including the need for optimization routines to accommodate noisy responses, repeated Bayesian inferences under different scenarios, and all while wrapping around an often computationally intensive and highly nonlinear physics-based forward model with high-dimensional input and output spaces.
Example: hydrogen-oxygen ignition
Below are expected utility contours approximated from using the full physical model (left), and a polynomial chaos surrogate model that greatly accelerate the computations (right); they show excellent agreement.
Three experiments are simulated (at designs A, B, and C in figure above). Bayesian inference is performed and posterior densities are shown below. Designs A (which has the highest expected utility value) produces the “tightest” posterior (left), reflecting a larger reduction in uncertainty and increased confidence for estimating these parameters.
Uncertainty quantification (UQ) plays a crucial role in engineering and science. It enters research and production workflows at various points, such as the highlighted yellow fields below. UQ is particularly important for the learning process, by providing a principled framework for interactions among theory, models, and data.
Example: reactive flows inside a scramjet combustor
Scramjets are propulsive systems that can enable efficient and stable operations under hypersonic flight conditions (Mach 5+). For instance, the figure below shows the flight test payload from the Hypersonic International Flight Research Experimentation (HIFiRE) program.
Designing scramjet engines requires both accurate flow simulations as well as UQ. Enabling UQ for expensive simulations of turbulent supersonic reactive flows faces many computational challenges. Techniques such as global sensitivity analysis, surrogate modeling, compressive sensing, efficient Bayesian calibration, and multilevel multifidelity sampling are needed to make UQ analysis possible. Below is an illustration of the predictive uncertainty (blue contours) of Mach number at select vertical stations of the combustor section. Such information would be valuable for finding optimal scramjet designs that are also robust and reliable.