Optimal Experimental Design Routines#
This package provides the classes and utilities needed to formulate and solve Optimal Experimental Design (OED) problems for various applications.
An OED problem seeks a design \(\zeta^*\) that optimizes an objective (optimality criterion) \(\Phi(\zeta)\) over a design space \(\mathcal{D}\):
Common criteria include A-optimality (trace of posterior covariance), D-optimality (log-determinant), and information-theoretic measures (expected information gain, KL divergence).
Our main assumption is that an OED problem involves an inverse problem (we focus on model-constrained OED), however, the base classes are extensible enough to relax this assumption.
Key OED Classes at a Glance#
Base class for implementations of OED (Optimal Experimental Design) methods/approaches. |
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The main class for all implementations of OED for Inverse Problems (Bayesian of not). |
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Base class for sensor placement OED for inverse problems. |
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This class provides a general implementation for sensor-placement OED in Bayesian inverse problems. |
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This is the main implementation of researech development on robust OED presented in [1]. |