Physical
Computation
Laboratory

We explore fundamental methods to use an understanding of the physical world to enable efficient computation that interacts with nature.

Physical Computation Laboratory

Recent Publications

Efficient Digital Methods to Quantify Sensor Output Uncertainty
Members Public

Efficient Digital Methods to Quantify Sensor Output Uncertainty
Distributional Computational Graphs: Error Bounds
Members Public

Distributional Computational Graphs: Error Bounds
Uncertainty Propagation in Finite Impulse Response Filters: Evaluating the Gaussian Assumption

Jennie Couchman and Phillip Stanley-Marbell. Preprint.      
Members Public

Uncertainty Propagation in Finite Impulse Response Filters: Evaluating the Gaussian Assumption
A Tensor Train Approach for Deterministic Arithmetic Operations on Discrete Representations of Probability Distributions
Members Public

A Tensor Train Approach for Deterministic Arithmetic Operations on Discrete Representations of Probability Distributions