Predicting Crack Nucleation and Propagation in Brittle Materials Using Deep Operator Networks with Diverse Trunk ArchitecturesPhase-field modeling reformulates fracture problems as energy minimization problems and enables a comprehensive characterization of the fracture process, including crack nucleation, propagation,…Elham Kiyani, Manav Manav, Nikhil Kadivar et al.·Dec 15, 2024SaveLearn
Energy-Efficient Sampling Using Stochastic Magnetic Tunnel Junctions(Pseudo)random sampling, a costly yet widely used method in (probabilistic) machine learning and Markov Chain Monte Carlo algorithms, remains unfeasible on a truly large scale due to unmet…Nicolas Alder, Shivam Nitin Kajale, Milin Tunsiricharoengul et al.·Dec 14, 2024SaveLearn
Finite difference physics-informed neural networks enable improved solution accuracy of the Navier-Stokes equationsGenerating an accurate solution of the Navier--Stokes equations using physics--informed neural networks (PINNs) for higher Reynolds numbers in the corners of a lid--driven cavity problem is…Nityananda Roy, Robert Dürr, Andreas Bück et al.·Dec 14, 2024SaveLearn
Computational Analysis of the Temperature Profile Developed for a Hot Zone of 2500C in an Induction FurnaceTemperature gradients developed at ultra-high temperatures create a challenge for temperature measurements that are required for material processing. At ultra-high temperatures, the components of the…Juan C. Herrera, Laura L. Sandoval, Piyush Kumar et al.·Dec 13, 2024SaveLearn
On-the-Fly Path Planning for the Design of Compositional Gradients in High DimensionsFunctional gradients have recently experienced an explosion in activity due to advances in manufacturing, where compositions can now be spatially varied on-the-fly during fabrication. In addition,…Samuel Price, Zhaoxi Cao, Ian McCue·Dec 13, 2024SaveLearn
Adapting Atmospheric Chemistry Components for Efficient GPU AcceleratorsAtmospheric models demand a lot of computational power and solving the chemical processes is one of its most computationally intensive components. This work shows how to improve the computational…Christian Guzman Ruiz, Matthew Dawson, Mario C. Acosta et al.·Dec 13, 2024SaveLearn
MADWAVE3: a quantum time dependent wave packet code for nonadiabatic state-to-state reaction dynamics of triatomic systemsWe present MADWAVE3, a FORTRAN90 code designed for quantum time dependent wave packet propagation in triatomic systems. This program allows the calculation of state-to-state probabilities for…Octavio Roncero, Pablo del Mazo-Sevillano·Dec 13, 2024SaveLearn
High-Speed Time Series Prediction with a GHz-rate Photonic Spiking Neural Network built with a single VCSELPhotonic technologies hold significant potential for creating innovative, high-speed, efficient and hardware-friendly neuromorphic computing platforms. Neuromorphic photonic methods leveraging…Dafydd Owen-Newns, Lina Jaurigue, Josh Robertson et al.·Dec 12, 2024SaveLearn
Effect of thermal fluctuations on the average shape of a graphene nanosheet suspended in a shear flowGraphene nanosheets display large hydrodynamic slip lengths in most solvents, and because of this, adopt a stable orientation in a shear flow instead of rotating when thermal fluctuations are…Simon Gravelle, Catherine Kamal, Lorenzo Botto·Dec 12, 2024SaveLearn
Machine Learning Enhanced Collision Operator for the Lattice Boltzmann Method Based on Invariant NetworksIntegrating machine learning techniques in established numerical solvers represents a modern approach to enhancing computational fluid dynamics simulations. Within the lattice Boltzmann method (LBM),…Mario Christopher Bedrunka, Tobias Horstmann, Ben Picard et al.·Dec 11, 2024SaveLearn
Threading in star catenanes: The role of ring rigidity, topology and environmental crowdingThis study investigates the probability of threading in star catenanes under good solvent conditions using molecular dynamics simulations, emphasizing the influence of ring rigidity. Threading in…Zahra Ahmadian Dehaghani·Dec 10, 2024SaveLearn
Arbitrary Lagrangian--Eulerian finite element method for lipid membranesAn arbitrary Lagrangian--Eulerian finite element method and numerical implementation for curved and deforming lipid membranes is presented here. The membrane surface is endowed with a mesh whose…Amaresh Sahu·Dec 10, 2024SaveLearn
Tunable Orbital Thermoelectric Transport with Spin-Valley Coupling in Ferromagnetic Transition Metal DichalcogenidesIn valleytronic devices, the valley transport of electrons can carry not only charge but also spin angular momentum (SAM) and orbital angular momentum (OAM). However, investigations on thermoelectric…Shilei Ji, Jianping Yang, Li Gao et al.·Dec 10, 2024SaveLearn
FE-PINNs: finite-element-based physics-informed neural networks for surrogate modelingWe present a method whereby the finite element method is used to train physics-informed neural networks that are suitable for surrogate modeling. The method is based on a custom convolutional…Pranav Sunil, Ryan B. Sills·Dec 10, 2024SaveLearn
PyPSA-Spain: an extension of PyPSA-Eur to model the Spanish energy systemThis work presents PyPSA-Spain, an open-source model of the Spanish energy system based on the European model PyPSA-Eur. It aims to leverage the benefits of single-country modelling over a…Cristobal Gallego-Castillo, Marta Victoria·Dec 9, 2024SaveLearn
An efficiency and memory-saving programming paradigm for the unified gas-kinetic schemeIn recent years, non-equilibrium flows have gained significant attention in aerospace engineering and micro-electro-mechanical systems. The unified gas-kinetic scheme (UGKS) follows the methodology…Yue Zhang, Yufeng Wei, Wenpei Long et al.·Dec 9, 2024SaveLearn
A Three-Tiered Hierarchical Computational Framework Bridging Molecular Systems and Junction-Level Charge TransportThe Non-Equilibrium Green's Function (NEGF) method combined with ab initio calculations has been widely used to study charge transport in molecular junctions. However, the significant…Xuan Ji, Qiang Qi, Yueqi Chen et al.·Dec 9, 2024SaveLearn
Scope of physics-based simulation artefactsData and metadata documentation requirements for explainable-AI-ready (XAIR) models and data in physics-based simulation technology are discussed by analysing different perspectives from the…Martin Thomas Horsch, Fadi Al Machot, Jadran Vrabec·Dec 8, 2024SaveLearn
Finite Element Neural Network Interpolation. Part II: Hybridisation with the Proper Generalised Decomposition for non-linear surrogate modellingThis work introduces a hybrid approach that combines the Proper Generalised Decomposition (PGD) with deep learning techniques to provide real-time solutions for parametrised mechanics problems. By…Alexandre Daby-Seesaram, Kateřina Škardová, Martin Genet·Dec 7, 2024SaveLearn
Power Laws for the Thermal Slip Length of a Liquid/Solid Interface From the Structure and Frequency Response of the Contact ZoneThe newest and most powerful electronic chips for applications like artificial intelligence generate so much heat that liquid based cooling has become indispensable to prevent breakdown from thermal…Hiroki Kaifu, Sandra M. Troian·Dec 6, 2024SaveLearn
Fully independent response in disordered solidsUnlike in crystals, it is difficult to trace emergent material properties of amorphous solids to their underlying structure. Nevertheless, one can tune features of a disordered spring network,…Mengjie Zu, Aayush Desai, Carl P. Goodrich·Dec 6, 2024SaveLearn
Self-Organizing Complex Networks with AI-Driven Adaptive Nodes for Optimized Connectivity and Energy EfficiencyHigh connectivity and robustness are critical requirements in distributed networks, as they ensure resilience, efficient communication, and adaptability in dynamic environments. Additionally,…Azra Seyyedi, Mahdi Bohlouli, SeyedEhsan Nedaaee Oskoee·Dec 6, 2024SaveLearn
Cost optimized ab initio tensor network state methods: industrial perspectivesWe introduce efficient solutions to optimize the cost of tree-like tensor network state method calculations when an expensive GPU-accelerated hardware is utilized. By supporting a main powerful…Andor Menczer, Örs Legeza·Dec 6, 2024SaveLearn
Numerical Aspects of Large DeviationsAn introduction to numerical large-deviation sampling is provided. First, direct biasing with a known distribution is explained. As simple example, the Bernoulli experiment is used throughout the…Alexander K. Hartmann·Dec 5, 2024SaveLearn
A Data-Driven Framework for Discovering Fractional Differential Equations in Complex SystemsIn complex physical systems, conventional differential equations often fall short in capturing non-local and memory effects, as they are limited to local dynamics and integer-order interactions. This…Xiangnan Yu, Hao Xu, Zhiping Mao et al.·Dec 5, 2024SaveLearn