| Multi-objective loss balancing for physics-informed deep learning R Bischof, M Kraus arXiv preprint arXiv:2110.09813, 2021 | 94 | 2021 |
| Parameter identification methods for visco-and hyperelastic material models MA Kraus, M Schuster, J Kuntsche, G Siebert, J Schneider Glass Structures & Engineering 2 (2), 147-167, 2017 | 70 | 2017 |
| Machine learning techniques for the material parameter identification of laminated glass in the intact and post-fracture state MA Kraus Universität der Bundeswehr, 2019 | 39* | 2019 |
| Artificial intelligence for structural glass engineering applications—overview, case studies and future potentials MA Kraus, M Drass Glass Structures & Engineering 5 (3), 247-285, 2020 | 35 | 2020 |
| Relationship between strain energy and fracture pattern morphology of thermally tempered glass for the prediction of the 2D macro-scale fragmentation of glass N Pourmoghaddam, MA Kraus, J Schneider, G Siebert Glass Structures & Engineering 4 (2), 257-275, 2019 | 34 | 2019 |
| Investigations on the thermorheologically complex material behaviour of the laminated safety glass interlayer ethylene-vinyl-acetate M Schuster, M Kraus, J Schneider, G Siebert Glass Structures & Engineering 3 (2), 373-388, 2018 | 25 | 2018 |
| Generalized collocation method using Stiffness matrices in the context of the Theory of Linear viscoelasticity (GUSTL) MA Kraus, M Niederwald Technische Mechanik-European Journal of Engineering Mechanics 37 (1), 82-106, 2017 | 22 | 2017 |
| Experimental determination of the shear modulus of polymeric interlayers used in laminated glass M Botz, MA Kraus, G Siebert Proceedings of GlassCon Global, Chicago, 31-38, 2018 | 13 | 2018 |
| The geometrical properties of random 2D Voronoi tesselations for the prediction of the tempered glass fracture pattern N Pourmoghaddam, MA Kraus, J Schneider, G Siebert ce/papers 2 (5-6), 325-339, 2018 | 12 | 2018 |
| Scientific machine and deep learning investigations of the local buckling behaviour of hollow sections A Müller, A Taras, MA Kraus ce/papers 5 (4), 1034-1042, 2022 | 11 | 2022 |
| Semantic segmentation with deep learning: detection of cracks at the cut edge of glass M Drass, H Berthold, MA Kraus, S Müller-Braun Glass Structures & Engineering 6 (1), 21-37, 2021 | 11 | 2021 |
| Automated quality control of vacuum insulated glazing by convolutional neural network image classification H Riedel, S Mokdad, I Schulz, C Kocer, PL Rosendahl, J Schneider, ... Automation in Construction 135, 104144, 2022 | 10 | 2022 |
| Mixture-of-experts-ensemble meta-learning for physics-informed neural networks R Bischof, MA Kraus Proceedings of 33. Forum Bauinformatik, 2022 | 10 | 2022 |
| Physik‐informierte Künstliche Intelligenz zur Berechnung und Bemessung im Stahlbau MA Kraus, A Taras Stahlbau 89 (10), 824-832, 2020 | 10 | 2020 |
| Semi-probabilistic calibration of a partial material safety factor for structural silicone adhesives—part I: derivation M Drass, MA Kraus Int. J. Struct. Glass Adv. Mater. Res 4 (1), 56-68, 2020 | 10 | 2020 |
| Untersuchungen zur thermomechanischen Modellierung der Resttragfähigkeit von Verbundglas M Botz, MA Kraus, G Siebert ce/papers 3 (1), 125-136, 2019 | 10 | 2019 |
| SoundLab AI-Machine learning for sound insulation value predictions of various glass assemblies M Drass, MA Kraus, H Riedel, I Stelzer Glass Structures & Engineering 7 (1), 101-118, 2022 | 9 | 2022 |
| Künstliche Intelligenz–multiskale und cross‐domäne Synergien von Raumfahrt und Bauwesen MA Kraus, M Drass, B Hörsch, J Schneider, W Kaufmann BetonKalender 2022: Nachhaltigkeit, Digitalisierung, Instandhaltung, 607-690, 2022 | 9 | 2022 |
| Mixed reality applications for teaching structural design MA Kraus, I Čustović, W Kaufmann Structures Congress 2022, 283-295, 2022 | 9 | 2022 |
| Dimensioning of silicone adhesive joints: Eurocode-compliant, mesh-independent approach using the FEM M Drass, MA Kraus Glass Structures & Engineering 5 (3), 349-369, 2020 | 9 | 2020 |
Dr. Michael DrassDeutsche Bahn AG | AI Factory在 mm-network-ing.com 的电子邮件经过验证
