Related papers: Technical Note: Radiotherapy dose calculations usi…
Previously, it has been shown that maximum-entropy models of immune-repertoire sequence can be used to determine a person's vaccination status. However, this approach has the drawback of requiring a computationally intensive method to…
We present an adaptive multi-GPU Exchange Monte Carlo method designed for the simulation of the 3D Random Field Model. The algorithm design is based on a two-level parallelization scheme that allows the method to scale its performance in…
Today's cloud infrastructure landscape offers a broad range of services to build and operate software applications. The myriad of options, however, has also brought along a new layer of complexity. When it comes to procuring cloud computing…
Objective: To develop and validate an independent Monet Carlo dose calculation engine to support for software verification of treatment planning systems and quality assurance workflow. Method: GATE Monte Carlo toolkit was employed to…
The XCAT phantom is a realistic 4D digital torso phantom that is widely used in imaging and therapy research. However, lung mass is not conserved between respiratory phases of the phantom, making detailed dosimetric simulations and dose…
Accurate 3D dose calculation for Pencil Beam Scanning Proton Therapy (PBSPT) is typically performed with Monte Carlo (MC) engines, but their runtimes limit adaptive workflows and repeated evaluations. Current deep-learning proton dose…
When calculating satellite trajectories in low-earth orbit, engineers need to adequately estimate aerodynamic forces. But to this day, obtaining the drag acting on the complicated shapes of modern spacecraft suffers from many sources of…
Automatic resource scaling is one advantage of Cloud systems. Cloud systems are able to scale the number of physical machines depending on user requests. Therefore, accurate request prediction brings a great improvement in Cloud systems'…
Financial derivatives are contracts that can have a complex payoff dependent upon underlying benchmark assets. In this work, we present a quantum algorithm for the Monte Carlo pricing of financial derivatives. We show how the relevant…
Due to a hard dependency between time steps, large-scale simulations of gas using the Direct Simulation Monte Carlo (DSMC) method proceed at the pace of the slowest processor. Scalability is therefore achievable only by ensuring that the…
A Monte Carlo fluence estimator has been designed to take advantage of the computational power of graphical processing units (GPUs). This new estimator, termed the volumetric-ray-casting estimator, is an extension of the expected-value…
Applications employed in the financial services industry to capture and estimate a variety of risk metrics are underpinned by stochastic simulations which are data, memory and computationally intensive. Many of these simulations are…
This study investigates the application of machine learning algorithms, particularly in the context of pricing American options using Monte Carlo simulations. Traditional models, such as the Black-Scholes-Merton framework, often fail to…
Cloud computing revolutionised the industry with its elastic, on-demand approach to computational resources, but has lead to a tremendous impact on the environment. Data centers constitute 1.1-1.5% of total electricity usage in the world.…
Cloud computing offers flexibility in resource provisioning, allowing an organization to host its batch processing workloads cost-efficiently by dynamically scaling the size and composition of a cloud-based cluster -- a collection of…
In Monte Carlo particle transport codes, it is often important to adjust reaction cross sections to reduce the variance of calculations of relatively rare events, in a technique known as non-analogous Monte Carlo. We present the theory and…
During the past years several variance reduction techniques for Monte Carlo electron transport have been developed in order to reduce the electron computation time transport for absorbed dose distribution. We have implemented the Macro…
The Kinetic Monte Carlo (KMC) method has become an important tool for examination of phenomena like surface diffusion and thin film growth because of its ability to carry out simulations for time scales that are relevant to experiments. But…
We implemented a fast Reciprocal Monte Carlo algorithm, to accurately solve radiative heat transfer in turbulent flows of non-grey participating media that can be coupled to fully resolved turbulent flows, namely to Direct Numerical…
We present SEDONA-GesaRaT, a rapid code for supernova radiative transfer simulation developed based on the Monte-Carlo radiative transfer code SEDONA. We use a set of atomic physics neural networks (APNN), an artificial intelligence (AI)…