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Supernova spectral time series can be used to reconstruct a spatially resolved explosion model known as supernova tomography. In addition to an observed spectral time series, a supernova tomography requires a radiative transfer model to…

Machine Learning · Computer Science 2022-09-21 Wolfgang Kerzendorf , Nutan Chen , Jack O'Brien , Johannes Buchner , Patrick van der Smagt

There are now hundreds of publicly available supernova spectral time series. Radiative transfer modeling of this data gives insights into the physical properties of these explosions such as the composition, the density structure, or the…

High Energy Astrophysical Phenomena · Physics 2020-01-15 C. Vogl , W. E. Kerzendorf , S. A. Sim , U. M. Noebauer , S. Lietzau , W. Hillebrandt

Type Ia supernovae remain poorly understood despite decades of investigation. Massive computationally intensive hydrodynamic simulations have been developed and run to model an ever-growing number of proposed progenitor channels. Further…

We present TARDIS - an open-source code for rapid spectral modelling of supernovae (SNe). Our goal is to develop a tool that is sufficiently fast to allow exploration of the complex parameter spaces of models for SN ejecta. This can be used…

Solar and Stellar Astrophysics · Physics 2015-06-18 Wolfgang E. Kerzendorf , Stuart A. Sim

Manual fits to spectral times series of Type Ia supernovae have provided a method of reconstructing the explosion from a parametric model but due to lack of information about model uncertainties or parameter degeneracies direct comparison…

In many mechanistic medical, biological, physical and engineered spatiotemporal dynamic models the numerical solution of partial differential equations (PDEs) can make simulations impractically slow. Biological models require the…

Soft Condensed Matter · Physics 2021-02-11 J. Quetzalcóatl Toledo-Marín , Geoffrey Fox , James P. Sluka , James A. Glazier

The inclusion of high-fidelity simulations of SOL turbulence and transient MHD events such as ELMs in highly iterative applications remains computationally prohibitive, limiting their use in design and control workflows. Understanding these…

Machine learning models of accelerator systems (`surrogate models') are able to provide fast, accurate predictions of accelerator physics phenomena. However, approaches to date typically do not include measured input diagnostics, such as…

Accelerator Physics · Physics 2021-04-06 Lipi Gupta , Auralee Edelen , Nicole Neveu , Aashwin Mishra , Christopher Mayes , Young-Kee Kim

Accurate models of the scrape-off layer are required for the design and operation of tokamak fusion reactors. Scrape-off layer simulations are computationally expensive, difficult to operate and suffer from numerical instabilities. A…

Plasma Physics · Physics 2026-04-22 Stefan Dasbach , Sebastijan Brezinsek , Yunfeng Liang , Dirk Reiser , Sven Wiesen

Recent efforts to improve the predictability of TALYS-2.0 calculated charged-particle residual product cross sections have focused on adjusting parameters related to the optical model potential and pre-equilibrium process. Although adjusted…

A challenging part of dynamic probabilistic risk assessment for nuclear power plants is the need for large amounts of temporal simulations given various initiating events and branching conditions from which representative feature extraction…

Machine Learning · Computer Science 2021-04-20 Bing Zha , Alessandro Vanni , Yassin Hassan , Tunc Aldemir , Alper Yilmaz

The computational resources required to solve the full 3D inversion of time-domain electromagnetic data are immense. To overcome the time-consuming 3D simulations, we construct a surrogate model, more precisely, a data-driven statistical…

Geophysics · Physics 2024-07-10 Wouter Deleersnyder , David Dudal , Thomas Hermans

Many physics and engineering applications demand Partial Differential Equations (PDE) property evaluations that are traditionally computed with resource-intensive high-fidelity numerical solvers. Data-driven surrogate models provide an…

Machine Learning · Computer Science 2023-12-18 Raphaël Pestourie , Youssef Mroueh , Chris Rackauckas , Payel Das , Steven G. Johnson

Programmers and researchers are increasingly developing surrogates of programs, models of a subset of the observable behavior of a given program, to solve a variety of software development challenges. Programmers train surrogates from…

Programming Languages · Computer Science 2023-09-22 Alex Renda , Yi Ding , Michael Carbin

In neural network surrogate solvers for electromagnetic simulations, accurately modeling resonant phenomena remains a central challenge. High-amplitude resonances generate strongly localized field patterns that deviate significantly from…

Optics · Physics 2026-05-19 Sunghyun Nam , Chan Y. Park , Min Seok Jang

One of the main challenges in modeling massive stars to the onset of core collapse is the computational bottleneck of nucleosynthesis during advanced burning stages. The number of isotopes formed requires solving a large set of…

Kilonovae are the electromagnetic transients created by the radioactive decay of freshly synthesized elements in the environment surrounding a neutron star merger. To study the fundamental physics in these complex environments, kilonova…

High-performance scientific simulations, important for comprehension of complex systems, encounter computational challenges especially when exploring extensive parameter spaces. There has been an increasing interest in developing deep…

Machine Learning · Computer Science 2024-07-15 Pradeep Bajracharya , Javier Quetzalcóatl Toledo-Marín , Geoffrey Fox , Shantenu Jha , Linwei Wang

We present substantial extensions to the Monte Carlo radiative transfer code TARDIS to perform spectral synthesis for type II supernovae. By incorporating a non-LTE ionization and excitation treatment for hydrogen, a full account of…

High Energy Astrophysical Phenomena · Physics 2019-01-09 C. Vogl , S. A. Sim , U. M. Noebauer , W. E. Kerzendorf , W. Hillebrandt

Numerical models based on physics represent the state-of-the-art in earth system modeling and comprise our best tools for generating insights and predictions. Despite rapid growth in computational power, the perceived need for higher model…

Machine Learning · Computer Science 2022-01-10 Kate Duffy , Thomas Vandal , Weile Wang , Ramakrishna Nemani , Auroop R. Ganguly
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