Uncertainty Quantification

Computing Contact Problems with Self-Conforming Hybrid Materials

Realize a novel hybrid material that self-conforms around an object of interest as a physical route for computing and reporting the object’s shape.

Bayesian model calibration and optimization of surfactant-polymer flooding

The physical models governing surfactant-polymer (SP) flooding process are subject to parametric uncertainties, accurate quantification of which is crucial for improved decision making. Moreover, history matching of SP flooding is an ill-posed …

Using data science to discover materials with extreme properties.

Discovery of high-temperature, oxidation resistant, complex, concentrated alloys via data science driven multi-resolution experiments and simulations.

Physics-Informed LeaRnIng for Multiscale Systems (PILgRIMS)

Baking known physics into machine learning algorithms.

Sociotechnical Systems to Enable Smart and Connected Energy-Aware Residential Communities

Incentivizing subsidized communities to consume less energy.

Deep UQ: Learning deep neural network surrogate models for high dimensional uncertainty quantification

State-of-the-art computer codes for simulating real physical systems are often characterized by vast number of input parameters. Performing uncertainty quantification (UQ) tasks with Monte Carlo (MC) methods is almost always infeasible because of the …

Design of electric machines with manufacturing uncertainties

Development of computational tools for electric machine design including manufacturing uncertainties.

Efficient Algorithms for Ultra-fast Detection of Power System Contingencies in the Transient Regime.

Ultra-fast detection of faults in the power grid.

Causal inference on Bayesian graphical networks

Learn potential causal structures with quantified uncertainties from observational data.

Gaussian processes with built-in dimensionality reduction: Applications to high-dimensional uncertainty propagation

Uncertainty quantification (UQ) tasks, such as model calibration, uncertainty propagation, and optimization under uncertainty, typically require several thousand evaluations of the underlying computer codes. To cope with the cost of simulations, one …