238 citations
- RWTH Aachen UniversityDE5 papers
- Centre National de la Recherche ScientifiqueFR2 papers
- Brunel University of LondonGB1 paper
- École PolytechniqueFR1 paper
- Forschungszentrum JülichDE1 paper
- Fraunhofer Institute for Laser TechnologyDE1 paper
- HUN-REN Wigner Research Centre for PhysicsHU1 paper
- IMDEA MaterialsES1 paper
- Institute for Solid State Physics and OpticsHU1 paper
- Institut Jean LamourFR1 paper
- Keio UniversityJP1 paper
- Laboratoire de Physique de la Matière CondenséeFR1 paper
9 papers
A Spectral-based Physics-informed Finite Operator Learning for Prediction of Mechanical Behavior of Microstructures
Ali Harandi, Hooman Danesh, Kevin Linka +2
A novel physics-informed operator learning technique based on spectral methods is introduced to model the complex behavior of heterogeneous materials. The Lippmann-Schwinger operat…
A finite element-based physics-informed operator learning framework for spatiotemporal partial differential equations on arbitrary domains
Yusuke Yamazaki, Ali Harandi, Mayu Muramatsu +5
We propose a novel finite element-based physics-informed operator learning framework that allows for predicting spatiotemporal dynamics governed by partial differential equations (…
Geometry of triple junctions during grain boundary premelting
M. Torabi Rad, G. Boussinot, M. Apel
Grain Boundaries (GB) whose energy is larger than twice the energy of the solid/liquid interface exhibit the premelting phenomenon, for which an atomically thin liquid layer develo…
Polyconvex anisotropic hyperelasticity with neural networks
Dominik K. Klein, Mauricio Fernández, Robert J. Martin +2
In the present work, two machine learning based constitutive models for finite deformations are proposed. Using input convex neural networks, the models are hyperelastic, anisotrop…
Comparing mesoscopic models for dendritic growth
Damien Tourret, Laszlo Sturz, Alexandre Viardin +1
We present a quantitative benchmark of multiscale models for dendritic growth simulations. We focus on approaches based on phase-field, dendritic needle network, and grain envelope…
Theory-training deep neural networks for an alloy solidification benchmark problem
M. Torabi Rad, A. Viardin, G. J. Schmitz +1
Deep neural networks are machine learning tools that are transforming fields ranging from speech recognition to computational medicine. In this study, we extend their application t…