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Computational RNA secondary structure prediction is rather well established. However, such prediction algorithms always depend on a large number of experimentally measured parameters. Here, we study how sensitive structure prediction…
We present self-consistent, axisymmetric core-collapse supernova simulations performed with the Prometheus-Vertex code for 18 pre-supernova models in the range of 11-28 solar masses, including progenitors recently investigated by other…
Data driven generative machine learning models have recently emerged as one of the most promising approaches for new materials discovery. While the generator models can generate millions of candidates, it is critical to train fast and…
We here focus on the behavior of supernovae that technically explode in 1D (spherical symmetry). When simulated in 3D, however, the outcomes of representative progenitors of this class are quite different in almost all relevant quantities.…
Transfer learning is beneficial for survival analysis, especially when the target study has a limited number of events. However, existing transfer learning methods rely on the restrictive assumption that the target and source studies share…
The explosion outcome and diagnostics of core-collapse supernovae depend sensitively on the nature of the stellar progenitor, but most studies to date have focused exclusively on one-dimensional, spherically-symmetric massive star…
RNA function crucially depends on its structure. Thermodynamic models currently used for secondary structure prediction rely on computing the partition function of folding ensembles, and can thus estimate minimum free-energy structures and…
The core-collapse supernova (CCSN) mechanism is fundamentally three-dimensional with instabilities, convection, and turbulence playing crucial roles in aiding neutrino-driven explosions. Simulations of CCNSe including accurate treatments of…
We propose an optimistic estimate to evaluate the best possible fitting performance of nonlinear models. It yields an optimistic sample size that quantifies the smallest possible sample size to fit/recover a target function using a…
We propose a versatile framework for survival analysis that combines advanced concepts from statistics with deep learning. The presented framework is based on piecewise exponential models and thereby supports various survival tasks, such as…
We present an initial report on 160 simulations of a highly simplified model of the post-bounce core-collapse supernova environment in three spatial dimensions (3D). We set different values of a parameter characterizing the impact of…
The structure of an RNA molecule plays a significant role in its biological function. Predicting structure given a one dimensional sequence of RNA nucleotide bases is a difficult and important problem. Many computer programs (known as in…
Structural failure time models are causal models for estimating the effect of time-varying treatments on a survival outcome. G-estimation and artificial censoring have been proposed to estimate the model parameters in the presence of…
Understanding microstructure-property relationships (MPRs) is essential for optimising the performance of multiphase composites. Image-based poro/micro-mechanical modelling provides a non-invasive approach to exploring MPRs, but the…
The prediction of upcoming events in industrial processes has been a long-standing research goal since it enables optimization of manufacturing parameters, planning of equipment maintenance and more importantly prediction and eventually…
Understanding the explosion mechanism and hydrodynamic evolution of core-collapse supernovae is a long-standing quest in astronomy. The asymmetries caused by the explosion are encoded into the line profiles which appear in the nebular phase…
Following a simulation approach of recent publications we explore the viability of the neutrino-heating explosion mechanism in dependence on the spatial dimension. Our results disagree with previous findings. While we also observe that…
We perform spherically-symmetric general-relativistic simulations of core collapse and the postbounce preexplosion phase in 32 presupernova stellar models of solar metallicity with zero-age-main-sequence masses of 12 M_{sun} to 120 M_{sun}.…
Predicting the local dynamics of supercooled liquids based purely on local structure is a key challenge in our quest for understanding glassy materials. Recent years have seen an explosion of methods for making such a prediction, often via…
We have performed ab initio neutrino radiation hydrodynamics simulations in three and two spatial dimensions (3D and 2D) of core-collapse supernovae from the same 15 $M_\odot$ progenitor through 440 ms after core bounce. Both 3D and 2D…