Zobrazeno 1 - 10
of 228
pro vyhledávání: '"Kollmannsberger, Stefan"'
Autor:
Singh, Divya Shyam, Herrmann, Leon, Sun, Qing, Bürchner, Tim, Dietrich, Felix, Kollmannsberger, Stefan
Full waveform inversion (FWI) is a powerful tool for reconstructing material fields based on sparsely measured data obtained by wave propagation. For specific problems, discretizing the material field with a neural network (NN) improves the robustnes
Externí odkaz:
http://arxiv.org/abs/2408.00695
Neural networks have recently been employed as material discretizations within adjoint optimization frameworks for inverse problems and topology optimization. While advantageous regularization effects and better optima have been found for some invers
Externí odkaz:
http://arxiv.org/abs/2407.17957
Fatigue simulation requires accurate modeling of unloading and reloading. However, classical ductile damage models treat deformations after complete failure as irrecoverable -- which leads to unphysical behavior during unloading. This unphysical beha
Externí odkaz:
http://arxiv.org/abs/2312.00564
The scaled boundary finite element method is known for its capability in reproducing highly-detailed solution fields. This, however, is only attainable in those cases where analytical solutions exist. Many others invoke the use of numerical methods t
Externí odkaz:
http://arxiv.org/abs/2311.14375
Immersed boundary methods simplify mesh generation by embedding the domain of interest into an extended domain that is easy to mesh, introducing the challenge of dealing with cells that intersect the domain boundary. Combined with explicit time integ
Externí odkaz:
http://arxiv.org/abs/2310.14712
The rapid growth of deep learning research, including within the field of computational mechanics, has resulted in an extensive and diverse body of literature. To help researchers identify key concepts and promising methodologies within this field, w
Externí odkaz:
http://arxiv.org/abs/2309.15421
Ductile damage models and cohesive laws incorporate the material plasticity entailing the growth of irrecoverable deformations even after complete failure. This unrealistic growth remains concealed until the unilateral effects arising from the crack
Externí odkaz:
http://arxiv.org/abs/2306.14038
Publikováno v:
Computer Methods in Applied Mechanics and Engineering, 2023
Full waveform inversion (FWI) is an iterative identification process that serves to minimize the misfit of model-based simulated and experimentally measured wave field data, with the goal of identifying a field of parameters for a given physical obje
Externí odkaz:
http://arxiv.org/abs/2305.19699
We propose a way to favorably employ neural networks in the field of non-destructive testing using Full Waveform Inversion (FWI). The presented methodology discretizes the unknown material distribution in the domain with a neural network within an ad
Externí odkaz:
http://arxiv.org/abs/2302.11259
Publikováno v:
Computer Methods in Applied Mechanics and Engineering, 2023
Neural networks have recently gained attention in solving inverse problems. One prominent methodology are Physics-Informed Neural Networks (PINNs) which can solve both forward and inverse problems. In the paper at hand, full waveform inversion is the
Externí odkaz:
http://arxiv.org/abs/2303.03260