1 citations · 1 across the 10 of their papers we have counts for
12 papers
Risk-Aware Goal-Oriented Bayesian Optimal Experimental Design
John D. Jakeman, Rebekah White, Bart van Bloemen Waanders +2
Traditional Bayesian optimal experimental design (OED) selects measurements that best inform a model's parameters. However, such measurements can be suboptimal for downstream predi…
Multi-type Sensor Placement for PDE-based Bayesian Inverse Problems
Steven Maio, Alen Alexanderian, Karina Koval +1
We address optimal placement of multi-type sensors for Bayesian inverse problems governed by partial differential equations (PDEs). The proposed framework allows for sensors with d…
FlexTrace: Exchangeable Randomized Trace Estimation for Matrix Functions
Madhusudan Madhavan, Alen Alexanderian, Arvind K. Saibaba
We consider the task of estimating the trace of a matrix function, , of a large symmetric positive semi-definite matrix . This problem arises in mult…
Submodularity of the expected information gain in infinite-dimensional linear inverse problems
Alen Alexanderian, Steven Maio
We consider infinite-dimensional linear Gaussian Bayesian inverse problems with uncorrelated measurement errors and focus on the problem of selecting sensor placements that maximiz…
Path-OED for infinite-dimensional Bayesian linear inverse problems governed by PDEs
J. Nicholas Neuberger, Alen Alexanderian, Bart van Bloemen Waanders +1
We consider infinite-dimensional Bayesian linear inverse problems governed by time-dependent partial differential equations (PDEs) and develop a mathematical and computational fram…
A brief note on submodular optimization
Alen Alexanderian
We consider some basic concepts concerning the optimization of monotone submodular functions, including a detailed discussion of submodularity. Illustrative examples are included t…
Preconditioned pseudo-time continuation for parameterized inverse problems
Joseph Hart, Alen Alexanderian, Bart van Bloemen Waanders
We consider parameterized variational inverse problems that are constrained by partial differential equations (PDEs). We seek to efficiently compute the solution of the inverse pro…
On submodularity of the expected information gain
Steven Maio, Alen Alexanderian
We consider finite-dimensional linear Gaussian Bayesian inverse problems with uncorrelated sensor measurements. In this setting, it is known that the expected information gain, qua…
Bridging the Gap Between Deterministic and Probabilistic Approaches to State Estimation
Lev Kakasenko, Alen Alexanderian, Mohammad Farazmand +1
We consider the problem of state estimation from limited discrete and noisy measurements. In particular, we focus on modal state estimation, which approximates the unknown state of…
A control-oriented approach to optimal sensor placement
Madhusudan Madhavan, Alen Alexanderian, Arvind K. Saibaba +2
We propose a control-oriented optimal experimental design (cOED) approach for linear PDE-constrained Bayesian inverse problems. In particular, we consider optimal control problems…
Optimal sensor placement under model uncertainty in the weak-constraint 4D-Var framework
Alen Alexanderian, Hugo Díaz, Vishwas Rao +1
In data assimilation, the model may be subject to uncertainties and errors. The weak-constraint data assimilation framework enables incorporating model uncertainty in the dynamics…
Goal oriented optimal design of infinite-dimensional Bayesian inverse problems using quadratic approximations
J. Nicholas Neuberger, Alen Alexanderian, Bart van Bloemen Waanders
We consider goal-oriented optimal design of experiments for infinite-dimensional Bayesian linear inverse problems governed by partial differential equations (PDEs). Specifically, w…