Extending the Thick Level Set Approach: Plasticity, Parallel Computing and Cohesive Cracks

Developing accurate and robust numerical approaches that are capable of modeling fracture in solids has been a challenging undertaking in the computational mechanics community for decades. Models based on a continuous formulation or on a discontinuous one have been proposed by numerous authors, expanding upon abilities and disadvantages of these...

An enhanced variable-fidelity optimization approach for constrained optimization problems and its parallelization

In this paper, a variable-fidelity constrained lower confidence bound (VF-CLCB) criterion is presented for computationally expensive constrained optimization problems (COPs) with two levels of fidelity. In VF-CLCB, the hierarchical Kriging model is adopted to model the objective and inequality constraints. Two infill sampling functions are...

Distributed memory parallel groundwater modeling for the Netherlands Hydrological Instrument

Worldwide, billions of people rely on fresh groundwater reserves for their domestic, agricultural and industrial water use. Extreme droughts and excessive groundwater pumping put pressure on water authorities in maintaining sustainable water usage. High-resolution integrated models are valuable assets in supporting them. The Netherlands...

Distributed memory parallel computing of three-dimensional variable-density groundwater flow and salt transport

Fresh groundwater reserves, being of vital importance for more than a billion of people living in the coastal zone, are being threatened by saltwater intrusion due to anthropogenic activities and climate change. High resolution three-dimensional (3D), variable-density (VD), groundwater flow and salt transport (FT) numerical models are...

Combining parallel computing and biased randomization for solving the team orienteering problem in real-time

In smart cities, unmanned aerial vehicles and self-driving vehicles are gaining increased concern. These vehicles might utilize ultra-reliable telecommunication systems, Internet-based technologies, and navigation satellite services to locate their customers and other team vehicles to plan their routes. Furthermore, the team of vehicles...

Accelerating a geometrical approximated PCA algorithm using AVX2 and CUDA

Remote sensing data has known an explosive growth in the past decade. This has led to the need for efficient dimensionality reduction techniques, mathematical procedures that transform the high-dimensional data into a meaningful, reduced representation. Projection Pursuit (PP) based algorithms were shown to be efficient solutions for...

OpenACC GPU implementation of double-stage delay-multiply-and-sum algorithm: Toward enhanced real-time linear-array photoacoustic tomography

Double-stage delay-multiply-and-sum (DS-DMAS) is one of the algorithms proposed for photoacoustic image reconstruction where a linear-array transducer is used to detect signals. This algorithm provides a higher contrast image in comparison with the conventional delay-multiply-and-sum (DMAS) and delay-and-sum (DAS), but it imposes a high...

A neural network-based framework for financial model calibration

A data-driven approach called CaNN (Calibration Neural Network) is proposed to calibrate financial asset price models using an Artificial Neural Network (ANN). Determining optimal values of the model parameters is formulated as training hidden neurons within a machine learning framework, based on available financial option prices. The...

Acceleration of the Multi Level Fast Multipole Algorithm on a Graphics Processing Unit

It is of great importance that enemy aircraft can be detected by a radar. Good knowledge about one’s own detectability is also needed to make one’s own visibility as low as possible. Obtaining the aircraft radar signature involves solving a scattering problem with a large, low-observable aircraft. Briefly explained; the incident radar wave...

Efficient GOCE satellite gravity field recovery based on least-squares using QR decomposition

We develop and apply an efficient strategy for Earth gravity field recovery from satellite gravity gradiometry data. Our approach is based upon the Paige-Saunders iterative least-squares method using QR decomposition (LSQR). We modify the original algorithm for space-geodetic applications: firstly, we investigate how convergence can be...