Neural operator-based super-fidelity: A warm-start approach for accelerating steady-state simulationsRecently, the use of neural networks to accelerate the solving of partial differential equations (PDEs) has gained significant traction in both academia and industry. However, employing neural…Xu-Hui Zhou, Jiequn Han, Muhammad I. Zafar et al.·Dec 19, 2023SaveLearn
Bifurcation tracking on moving meshes and with consideration of azimuthal symmetry breaking instabilitiesWe present a black-box method to numerically investigate the linear stability of arbitrary multi-physics problems. While the user just has to enter the system's residual in weak formulation, i.e. by…Christian Diddens, Duarte Rocha·Dec 18, 2023SaveLearn
Inconsistencies in Unstructured Geometric Volume-of-Fluid Methods for Two-Phase Flows with High Density RatiosGeometric flux-based Volume-of-Fluid (VOF) methods are widely considered consistent in handling two-phase flows with high density ratios. However, although the conservation of mass and momentum is…Jun Liu, Tobias Tolle, Davide Zuzio et al.·Dec 16, 2023SaveLearn
Ghostbuster: a phase retrieval diffraction tomography algorithm for cryo-EMEwald sphere curvature correction, which extends beyond the projection approximation, stretches the shallow depth of field in cryo-EM reconstructions of thick particles. Here we show that even for…Joel Yeo, Benedikt J. Daurer, Dari Kimanius et al.·Dec 14, 2023SaveLearn
-Diffusion: A diffusion-based density estimation framework for computational physicsIn physics, density (·) is a fundamentally important scalar function to model, since it describes a scalar field or a probability density function that governs a physical process. Modeling…Maxwell X. Cai, Kin Long Kelvin Lee·Dec 13, 2023SaveLearn
On the calculation of irregular solutions of the Schr\"odinger equation for non-spherical potentialsThe irregular solutions of the stationary Schr\"odinger equation are important for the fundamental formal development of scattering theory. They are also necessary for the analytical properties of…Rudolf Zeller·Dec 13, 2023SaveLearn
Gappy AE: A Nonlinear Approach for Gappy Data Reconstruction using Auto-EncoderWe introduce a novel data reconstruction algorithm known as Gappy auto-encoder (Gappy AE) to address the limitations associated with Gappy proper orthogonal decomposition (Gappy POD), a widely used…Youngkyu Kim, Youngsoo Choi, Byounghyun Yoo·Dec 13, 2023SaveLearn
Acceleration beyond lowest order event generation: An outlook on further parallelism within MadGraph5aMC@NLOAn important area of high energy physics studies at the Large Hadron Collider (LHC) currently concerns the need for more extensive and precise comparison data. Important tools in this realm are event…Zenny Wettersten, Olivier Mattelaer, Stefan Roiser et al.·Dec 12, 2023SaveLearn
The Toolkit for Nuclei library (TkN): a C++ interface to nuclear databasesOver the past few decades, a vast amount of information on the structure of atomic nuclei has been collected, compiled, and evaluated. Accurate and reliable data are essential for the understanding…Jérémie Dudouet, Diego Gruyer·Dec 12, 2023SaveLearn
A novel paradigm for solving PDEs: multi scale neural computingNumerical simulation is dominant in solving partial difference equations (PDEs), but balancing fine-grained grids with low computational costs is challenging. Recently, solving PDEs with neural…Wei Suo, Weiwei Zhang·Dec 12, 2023SaveLearn
Developing Parameter-Reduction Methods on a Biophysical Model of Auditory Hair CellsBiophysical models describing complex, cellular phenomena typically include systems of nonlinear differential equations with many free parameters. While experimental measurements can fix some…Joseph M. Marcinik, Martín A. Toderi, Dolores Bozovic·Dec 12, 2023SaveLearn
Geant4 Silver Anniversary: 25 years enabling scientific productionThis paper summarizes Geant4 contribution to scientific research over the past 25 years through a scientometric analysis of the results with which it has been associated. The scientometric data…Tullio Basaglia, Zane W. Bell, Daniele DAgostino et al.·Dec 11, 2023SaveLearn
The improved backward compatible physics-informed neural networks for reducing error accumulation and applications in data-driven higher-order rogue wavesDue to the dynamic characteristics of instantaneity and steepness, employing domain decomposition techniques for simulating rogue wave solutions is highly appropriate. Wherein, the backward…Shuning Lin, Yong Chen·Dec 11, 2023SaveLearn
Higher-Order Equivariant Neural Networks for Charge Density Prediction in MaterialsThe calculation of electron density distribution using density functional theory (DFT) in materials and molecules is central to the study of their quantum and macro-scale properties, yet accurate and…Teddy Koker, Keegan Quigley, Eric Taw et al.·Dec 8, 2023SaveLearn
Robust self-assembly of nonconvex shapes in 2DWe present fast simulation methods for the self-assembly of complex shapes in two dimensions. The shapes are modeled via a general boundary curve and interact via a standard volume term promoting…Lukas Mayrhofer, Myfanwy E. Evans, Gero Friesecke·Dec 8, 2023SaveLearn
Application of machine learning technique for a fast forecast of aggregation kinetics in space-inhomogeneous systemsModeling of aggregation processes in space-inhomogeneous systems is extremely numerically challenging since complicated aggregation equations -- Smoluchowski equations are to be solved at each space…M. A. Larchenko, R. R. Zagidullin, V. V. Palyulin et al.·Dec 7, 2023SaveLearn
Generalized Finite Differences Method Applied to Finite Photonic CrystalWe propose a Generalized Finite-Differences in the Frequency Domain method for the computation of photonic band structures of finite photonic crystals. Our approach is to discretize some fundamental…Santiago Bustamante, Esteban Marulanda, Jorge Mahecha et al.·Dec 6, 2023SaveLearn
Madgraph5aMC@NLO on GPUs and vector CPUs Experience with the first alpha releaseMadgraph5aMC@NLO is one of the most-frequently used Monte-Carlo event generators at the LHC, and an important consumer of compute resources. The software has been reengineered to maintain the…Stephan Hageboeck, Taylor Childers, Walter Hopkins et al.·Dec 5, 2023SaveLearn
Applications of Domain Adversarial Neural Network in phase transition of 3D Potts modelMachine learning techniques exhibit significant performance in discriminating different phases of matter and provide a new avenue for studying phase transitions. We investigate the phase transitions…Xiangna Chen, Feiyi Liu, Weibing Deng et al.·Dec 5, 2023SaveLearn
Switchable band topology and geometric current in sliding bilayer elemental ferroelectricWe demonstrate that sliding motion between two layers of the newly discovered ferroelectric and topologically trivial bismuth (Bi) monolayer [Nature 617, 67 (2023)] can induce a sequence of…Zhuang Qian, Zhihao Gong, Jian Li et al.·Dec 4, 2023SaveLearn
Uncertainty-biased molecular dynamics for learning uniformly accurate interatomic potentialsEfficiently creating a concise but comprehensive data set for training machine-learned interatomic potentials (MLIPs) is an under-explored problem. Active learning, which uses biased or unbiased…Viktor Zaverkin, David Holzmüller, Henrik Christiansen et al.·Dec 3, 2023SaveLearn
Thermally Averaged Magnetic Anisotropy Tensors via Machine Learning Based on Gaussian MomentsWe propose a machine learning method to model molecular tensorial quantities, namely the magnetic anisotropy tensor, based on the Gaussian-moment neural-network approach. We demonstrate that the…Viktor Zaverkin, Julia Netz, Fabian Zills et al.·Dec 3, 2023SaveLearn
Predicting Properties of Periodic Systems from Cluster Data: A Case Study of Liquid WaterThe accuracy of the training data limits the accuracy of bulk properties from machine-learned potentials. For example, hybrid functionals or wave-function-based quantum chemical methods are readily…Viktor Zaverkin, David Holzmüller, Robin Schuldt et al.·Dec 3, 2023SaveLearn
Asymptotic-preserving gyrokinetic implicit particle-orbit integrator for arbitrary electromagnetic fieldsWe extend the asymptotic preserving and energy conserving time integrator for charged-particle motion developed in [Ricketson & Chac\'on, JCP, 2020] to include finite Larmor-radius (FLR) effects in…Lee Ricketson, Luis Chacón·Dec 1, 2023SaveLearn
Ensemble variational Monte Carlo for optimization of correlated excited state wave functionsVariational Monte Carlo methods have recently been applied to the calculation of excited states; however, it is still an open question what objective function is most effective. A promising approach…William A. Wheeler, Kevin G. Kleiner, Lucas K. Wagner·Dec 1, 2023SaveLearn