Related papers: Global Sensitivity Analysis of a coupled multiphys…
In this work, we present a multi-fidelity, physics-informed framework for tritium fuel cycle modelling based on the open-source PathSim/PathView platform. Three complementary modelling approaches are demonstrated within a unified dynamic…
Atmosphere modelling applications become increasingly memory-bound due to the inconsistent development rates between processor speeds and memory bandwidth. In this study, we mitigate memory bottlenecks and reduce the computational load of…
The physics-based modeling has been the workhorse for many decades in many scientific and engineering applications ranging from wind power, weather forecasting, and aircraft design. Recently, data-driven models are increasingly becoming…
Machine learning (ML) models for predicting gas permeability through polymers have traditionally relied on experimental data. While these models exhibit robustness within familiar chemical domains, reliability wanes when applied to new…
Characterizing the connection between material design decisions/parameters and their effective properties allows for accelerated materials development and optimization. We present a global sensitivity analysis of woven composite…
Collider experiments, such as those at the Large Hadron Collider, use the Geant4 toolkit to simulate particle-detector interactions with high accuracy. However, these experiments increasingly require larger amounts of simulated data,…
We show that density models describing multiple observables with (i) hard boundaries and (ii) dependence on external parameters may be created using an auto-regressive Gaussian mixture model. The model is designed to capture how observable…
High-energy heavy-ion physics and low-energy nuclear structure physics have historically been disconnected fields. The hydrodynamic description of the quark-gluon plasma (QGP) requires input from nuclear structure to model the initial…
[Abridged] We present an application of a statistical tool known as Sensitivity Analysis to characterize the relationship between input parameters and observational predictions of semi-analytic models of galaxy formation coupled to…
Recently, a new probabilistic "data fusion" framework based on Bayesian principles has been developed on JET and W7-AS. The Bayesian analysis framework folds in uncertainties and inter-dependencies in the diagnostic data and signal…
We extend the well-studied midrapidity TRENTo initial-conditions model to three dimensions, thus facilitating (3+1)D modeling and analysis of ultrarelativistic heavy-ion collisions at RHIC and LHC energies. TRENTo-3D is a fast, parametric…
The absorbed laser energy of a femtosecond laser pulse in a transparent material induces a warm dense matter region which relaxation may lead to structural modifications in the surrounding cold matter. The modeling of the…
Soft pair potentials predict a reentrant liquid phase for high concentrations, a behavior not observed experimentally. Here, very soft microgels confined at an oil-water interface are used as a model system of particles interacting via a…
Reactive chemistry of molecular hydrogen at surfaces, notably dissociative sticking and hydrogen evolution, plays a crucial role in energy storage and fuel cells. Theoretical studies can help to decipher underlying mechanisms and reaction…
In the health domain, decisions are often based on different data modalities. Thus, when creating prediction models, multimodal fusion approaches that can extract and combine relevant features from different data modalities, can be highly…
The thermal model is commonly used in two different ways for the description of hadron production in ultra-relativistic heavy ion collision. One is the application of the thermal model to 4pi integrated data and the other is the thermal…
Thermophysical properties of hydrogen, helium, and hydrogen-helium mixtures have been investigated in the warm dense matter regime at electron number densities ranging from $6.02\times10^{29}\sim2.41\times10^{30}$/m$^{3}$ and temperatures…
Complex systems such as aircraft engines, turbines, and industrial machinery often operate under dynamically changing conditions. These varying operating conditions can substantially influence degradation behavior and make prognostic…
In this thesis, we explore the phenomenology of scalar particles within Beyond Standard Model frameworks, using Machine Learning (ML) techniques to enhance sensitivity and discovery potential at current and future collider experiments, the…
Many modern data sets are sampled with error from complex high-dimensional surfaces. Methods such as tensor product splines or Gaussian processes are effective/well suited for characterizing a surface in two or three dimensions but may…