4 papers
Modeling diesel output particulate matter as the Ornstein-Uhlenbeck process
Maxwell Bolt, Alex Alberts, Akash S. Desai +2
Diesel engine particulate matter (PM) is one of the most challenging emission constituents to predict. As engines become cleaner and emissions levels drop, manufacturers need relia…
Bayesian identification of fibrous insulation thermal conductivity towards design of spacecraft thermal protection systems
Alex Alberts, Akshay Jacob Thomas, Kamran Daryabeigi +1
The design of spacecraft thermal protection systems (TPS) requires accurate knowledge of thermal transport properties across wide ranges of temperature and pressure. For fibrous in…
Bayesian neural networks with interpretable priors from Mercer kernels
Alex Alberts, Ilias Bilionis
Quantifying the uncertainty in the output of a neural network is essential for deployment in scientific or engineering applications where decisions must be made under limited or no…
An interpretation of the Brownian bridge as a physics-informed prior for the Poisson equation
Alex Alberts, Ilias Bilionis
Many inverse problems require reconstructing physical fields from limited and noisy data while incorporating known governing equations. A growing body of work within probabilistic…