Dynamical system state estimation and parameter calibration problems are...
Inverse problems, i.e., estimating parameters of physical models from
ex...
Data-driven approaches coupled with physical knowledge are powerful
tech...
We present a Bayesian methodology to infer the elastic modulus of the
co...
The optimal design of magnetic devices becomes intractable using current...
Probabilistic machine learning models are often insufficient to help wit...
Reliable platforms for data collation during airline schedule operations...
Excessive loads near wounds produce pathological scarring and other
comp...
Estimating arbitrary quantities of interest (QoIs) that are non-linear
o...
After a disaster, teams of structural engineers collect vast amounts of
...
Systems engineering processes coordinate the effort of different individ...
Thermal preferences vary from person to person and may change over time....
We present a principal-agent model of a one-shot, shallow, systems
engin...
A problem of considerable importance within the field of uncertainty
qua...
Acquiring information about noisy expensive black-box functions (compute...
State-of-the-art computer codes for simulating real physical systems are...
The classical approach to inverse problems is based on the optimization ...