We propose a hierarchical parallelization strategy to improve the scalability of inverse multiple-scattering solutions. The inverse solver parallelizes the independent forward solutions corresponding to different illuminations. For further scaling out on large numbers of computing nodes, each forward solver parallelizes the dense and large matrix-vector multiplications accelerated by the multil...

Diffuse optical tomography (DOT) in the near infrared involves the reconstruction of spatially varying optical properties of a turbid medium from boundary measurements based on a forward model of photon propagation. Due to the nonlinear nature of DOT, high quality image reconstruction is a computationally demanding problem which requires repeated use of forward and inverse solvers. Therefore, i...

In this paper, we combine a fast wave equation solver using boundary integral methods with a global optimization method, namely Particle Swarm Optimization (PSO), to estimate an initial velocity model. Unlike finite difference methods that discretize the model space into pixels or voxels, our forward solver achieves significant computational savings by constraining the model space to a layered ...

The forward full-wave modeling of ground-penetrating radar (GPR) facilitates the understanding and interpretation GPR data. Traditional solvers require excessive computational resources, especially when their repetitive executions are needed in signal processing and/or machine learning algorithms for data inversion. To alleviate burden, a deep learning-based 2D solver is proposed to predict B-s...

This paper develops meshless methods for probabilistically describing discretisation error in the numerical solution of partial differential equations. This construction enables the solution of Bayesian inverse problems while accounting for the impact of the discretisation of the forward problem. In particular, this drives statistical inferences to be more conservative in the presence of signif...

We propose a new integer-linear programming model for the delete relaxation in cost-optimal planning. While a naive formulation of the delete relaxation as IP is impractical, our model incorporates landmarks and relevance-based constraints, resulting in an IP that can be used to directly solve the delete relaxation. We show that our IP model outperforms the previous state-of-the-art solver for ...

An attack graph represents all known sequences of actions that compromise a system in form of an and-or graph. We assume that each action in the attack graph has a specified cost and probability of success and propose an algorithm for computing an action selection policy minimizing the expected cost of performing an attack. We model the problem as a finite horizon MDP and use forward search wit...

An important technique for computing motions for robot systems is to conduct a numerical search for a trajectory that minimizes a physical criteria like energy, control effort, jerk, or time. In this paper, we provide example solutions of these types of optimal control problems, and develop a framework to solve these problems reliably. Our approach uses an efficient solver for both inverse and ...