An Adaptive Genetic Algorithm for determining optimal structures for atomic clustersThe implementation of adaptive genetic algorithms (AGA) for optimization problems has proven to be superior than many other methods due to its nature of producing more robust and high quality…Brandon Willnecker, Mervlyn Moodley·Nov 27, 2024SaveLearn
Maximum entropy mediated liquid-to-solid nucleation and transitionMolecular Dynamics (MD) simulations are a powerful tool for studying matter at the atomic scale. However, to simulate solids, an initial atomic structure is crucial for the successful execution of MD…Lars Dammann, Richard Kohns, Patrick Huber et al.·Nov 26, 2024SaveLearn
ACTest: A testing toolkit for analytic continuation methods and codesACTest is an open-source toolkit developed in the Julia language. Its central goal is to automatically establish analytic continuation testing datasets, which include a large number of spectral…Li Huang·Nov 25, 2024SaveLearn
A novel discontinuous-Galerkin deterministic neutronics model for Fusion applications: development and benchmarkingNeutron interactions in a fusion power plant play a pivotal role in determining critical design parameters such as coil-plasma distance and breeding blanket composition. Fast predictive neutronic…Timo Jos Bogaarts, Felix Warmer·Nov 25, 2024SaveLearn
High-order Discontinuous Galerkin solver based on Jacobi polynomial expansion for compressible flows on unstructured meshesBased on the Jacobi polynomial expansion, an arbitrary high-order Discontinuous Galerkin solver for compressible flows on unstructured meshes is proposed in the present work. First, we construct…Yu-Xiang Peng, Biao Wang, Peng-Nan Sun et al.·Nov 24, 2024SaveLearn
Capacitive Touch Sensor Modeling With a Physics-informed Neural Network and Maxwell's EquationsMaxwell's equations are the fundamental equations for understanding electric and magnetic field interactions and play a crucial role in designing and optimizing sensor systems like capacitive touch…Ganyong Mo, Krishna Kumar Narayanan, David Castells-Rufas et al.·Nov 23, 2024SaveLearn
An additive Mori-Tanaka scheme for elastic-viscoplastic composites based on a modified tangent linearizationMean-field modeling based on the Eshelby inclusion problem poses some difficulties when the non-linear Maxwell-type constitutive law is used for elasto-viscoplasticity. One difficulty is that this…Katarzyna Kowalczyk-Gajewska, Stephane Berbenni, Sebastien Mercier·Nov 22, 2024SaveLearn
Predicting rigidity and connectivity percolation in disordered particulate networks using graph neural networksGraph neural networks can accurately predict the chemical properties of many molecular systems, but their suitability for large, macromolecular assemblies such as gels is unknown. Here, graph neural…D. A. Head·Nov 21, 2024SaveLearn
ALKPU: an active learning method for the DeePMD model with Kalman filterNeural network force field models such as DeePMD have enabled highly efficient large-scale molecular dynamics simulations with ab initio accuracy. However, building such models heavily depends on the…Haibo Li, Xingxing Wu, Liping Liu et al.·Nov 21, 2024SaveLearn
Renormalization of States and Quasiparticles in Many-body DownfoldingWe explore the principles of many-body Hamiltonian complexity reduction via downfolding on an effective low-dimensional representation. We present a unique measure of fidelity between the effective…Annabelle Canestraight, Zhen Huang, Vojtech Vlcek·Nov 20, 2024SaveLearn
Integration of Active Learning and MCMC Sampling for Efficient Bayesian Calibration of Mechanical PropertiesRecent advancements in Markov chain Monte Carlo (MCMC) sampling and surrogate modelling have significantly enhanced the feasibility of Bayesian analysis across engineering fields. However, the…Leon Riccius, Iuri B. C. M. Rocha, Joris Bierkens et al.·Nov 20, 2024SaveLearn
An efficient, adaptive solver for accurate simulation of multicomponent shock-interface problems for thermally perfect speciesA second-order-accurate finite volume method, hybridized by blending an extended double-flux algorithm and a traditionally conservative scheme, is developed. In this scheme, hybrid convective fluxes…Yuqi Wang, Ralf Deiterding, Jianhan Liang·Nov 20, 2024SaveLearn
Recovering Mullins damage hyperelastic behaviour with physics augmented neural networksThe aim of this work is to develop a neural network for modelling incompressible hyperelastic behaviour with isotropic damage, the so-called Mullins effect. This is obtained through the use of…Martin Zlatić, Marko Čanađija·Nov 20, 2024SaveLearn
Learning Generalized Diffusions using an Energetic Variational ApproachExtracting governing physical laws from computational or experimental data is crucial across various fields such as fluid dynamics and plasma physics. Many of those physical laws are dissipative due…Yubin Lu, Xiaofan Li, Chun Liu et al.·Nov 20, 2024SaveLearn
Combining Hyperbolic Quadrature Method of Moments and Discrete-Velocity-Direction Models for Solving BGK-type EquationsThis paper introduces the discrete-velocity-direction model (DVDM) in conjunction with the hyperbolic quadrature method of moments (HyQMOM) to develop a multidimensional spatial-temporal…Tianshu Li, Yihong Chen, Qian Huang·Nov 19, 2024SaveLearn
A Generalized Flux-Corrected Transport Algorithm I: A Finite-Difference FormulationThis paper presents a generalized flux-corrected transport (FCT) algorithm, which is shown to be total variation diminishing under some conditions. The new algorithm has improved properties from the…William J Rider, Dennis R Liles·Nov 19, 2024SaveLearn
KAN/MultKAN with Physics-Informed Spline fitting (KAN-PISF) for ordinary/partial differential equation discovery of nonlinear dynamic systemsMachine learning for scientific discovery is increasingly becoming popular because of its ability to extract and recognize the nonlinear characteristics from the data. The black-box nature of deep…Ashish Pal, Satish Nagarajaiah·Nov 18, 2024SaveLearn
NeuralMag: an open-source nodal finite-difference code for inverse micromagneticsWe present NeuralMag, a flexible and high-performance open-source Python library for micromagnetic simulations. NeuralMag leverages modern machine learning frameworks, such as PyTorch and JAX, to…Claas Abert, Florian Bruckner, Andrey Voronov et al.·Nov 18, 2024SaveLearn
Neutron Counting Statistics calculations Using Deterministic TransportFor a number of applications like low-source reactor start-up or neutron coincidence counting it is necessary to take into account the stochastic nature of neutron transport and go beyond the average…Philippe Humbert·Nov 17, 2024SaveLearn
Escape-from-a-layer approach for simulating the boundary local time in Euclidean domainsWe propose an efficient numerical approach to simulate the boundary local time of reflected Brownian motion, as well as the time and position of the associated reaction event on a smooth boundary of…Yilin Ye, Adrien Chaigneau, Denis S. Grebenkov·Nov 15, 2024SaveLearn
A Sinking Approach to Explore Arbitrary Areas in Free Energy LandscapesTo address the time-scale limitations in molecular dynamics (MD) simulations, numerous enhanced sampling methods have been developed to expedite the exploration of complex free energy landscapes. A…Zhijun Pan, Maodong Li, Dechin Chen et al.·Nov 14, 2024SaveLearn
Linearization Routines for the Parameter Space Concept to determine Crystal Structures without Fourier Inversion (Centrosymmetric cases in two and three-dimensional parameter space)We present detailed elaboration and first generally applicable linearization routines of the Parameter Space Concept (PSC) for determining 1-dimensionally projected structures of m…Muthu Vallinayagam, Melanie Nentwich, Dirk C. Meyer et al.·Nov 13, 2024SaveLearn
Multiscale simulation of neutral particle flows in the plasma edgeThe plasma edge flow, situated at the intricate boundary between plasma and neutral particles, plays a pivotal role in the design of nuclear fusion devices such as divertors and pumps. Traditional…Yifan Wen, Yanbing Zhang, Lei Wu·Nov 13, 2024SaveLearn
Hybrid finite element implementation of two-potential constitutive modeling of dielectric elastomersDielectric elastomers are increasingly studied for their potential in soft robotics, actuators, and haptic devices. Under time-dependent loading, they dissipate energy via viscous deformation and…Kamalendu Ghosh, Bhavesh Shrimali·Nov 12, 2024SaveLearn
Automatic Identification of Traps in Molecular Charge Transport Networks of Organic SemiconductorsThis paper introduces a method to identify traps in molecular charge transport networks as obtained by multiscale modeling of organic semiconductors. Depending on the materials, traps can be…Zhongquan Chen, Pim van der Hoorn, Björn Baumeier·Nov 11, 2024SaveLearn