Related papers: Exploring Vainshtein mechanism on adaptively refin…
This paper proposes a general framework of Riemannian adaptive optimization methods. The framework encapsulates several stochastic optimization algorithms on Riemannian manifolds and incorporates the mini-batch strategy that is often used…
Gaussian processes (GPs) are Bayesian nonparametric models for function approximation with principled predictive uncertainty estimates. Deep Gaussian processes (DGPs) are multilayer generalizations of GPs that can represent complex marginal…
Model-independent constraints on modified gravity models hitherto exist mainly on linear scales. A recently developed formalism presented a consistent parameterisation that is valid on all scales. Using this approach, we perform…
Modifications of gravity have been considered to model the primordial inflation and the late-time cosmic acceleration. Provided that modified gravity models do not suffer from theoretical instabilities, they must be confronted with…
3D reconstruction and simulation, although interrelated, have distinct objectives: reconstruction requires a flexible 3D representation that can adapt to diverse scenes, while simulation needs a structured representation to model motion…
We present results of 3D numerical simulations using a finite difference code featuring fixed mesh refinement (FMR), in which a subset of the computational domain is refined in space and time. We apply this code to a series of test cases…
The Galileon theory is a prototypical effective field theory that incorporates the Vainshtein screening mechanism--a feature that arises in some extensions of General Relativity, such as massive gravity. The Vainshtein effect requires that…
We consider problems related to initial meshing and adaptive mesh refinement for the electromagnetic simulation of various structures. The quality of the initial mesh and the performance of the adaptive refinement are of great importance…
Theoretical differences in the growth of structure offer the possibility that we might distinguish between modified gravity theories of dark energy and \LambdaCDM. A significant impediment to applying current and prospective large scale…
We use N-body simulations to study the statistics of massive halos and redshift space distortions for theories with a standard \Lambda CDM expansion history and a galileon-type scalar field. The extra scalar field increases the…
The large time and length scales and, not least, the vast number of particles involved in industrial-scale simulations inflate the computational costs of the Discrete Element Method (DEM) excessively. Coarse grain models can help to lower…
Cosmological simulations of structure formation follow the collisionless evolution of dark matter starting from a nearly homogeneous field at early times down to the highly clustered configuration at redshift zero. The density field is…
We describe and demonstrate the potential of a new and very efficient method for simulating certain classes of modified gravity theories, such as the widely studied $f(R)$ gravity models. High resolution simulations for such models are…
We study the cosmology of a galileon scalar-tensor theory, obtained by covariantizing the decoupling lagrangian of the Dvali-Gabadadze-Poratti (DGP) model. Despite being local in 3+1 dimensions, the resulting cosmological evolution is…
Software vulnerability detection is critical for ensuring software security and reliability. Despite recent advances in deep learning, real-world vulnerability datasets suffer from two severe challenges: frequency imbalance and difficulty…
Spacetime discontinuous Galerkin (SDG) finite element methods are used to solve such PDEs involving space and time variables arising from wave propagation phenomena in important applications in science and engineering. To support an…
Data-driven modeling approaches can produce fast surrogates to study large-scale physics problems. Among them, graph neural networks (GNNs) that operate on mesh-based data are desirable because they possess inductive biases that promote…
Gravity inversion is the problem of estimating subsurface density distributions from observed gravitational field data. We consider the two-dimensional (2D) case, in which recovering density models from one-dimensional (1D) measurements…
Weak gravitational lensing is one of the primary cosmological probes, providing powerful constraints on the cosmological model. As Stage IV surveys are expected to deliver data of unprecedented precision, accurate modeling of weak…
In this paper, a novel adaptive finite element method is proposed to solve the Kohn-Sham equation based on the moving mesh (nonnested mesh) adaptive technique and the augmented subspace method. Different from the classical self-consistent…