5 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 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…
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…
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…
Uniqueness of MAP estimates for inverse problems under information field theory
Alex Alberts, Ilias Bilionis
Information field theory (IFT) is an emerging technique for posing infinite-dimensional inverse problems using the mathematics found in quantum field theory. Under IFT, the field i…