3 papers
physics.comp-ph2025
Design of a specimen to train path-dependent deep learning material models from a single uniaxial test: eliciting strain diversity via automatically differentiable elastoplastic topology optimization
Shunyu Yin, Bernardo P. Ferreira, Gawel Kus +1
Artificial neural networks accurately learn nonlinear, path-dependent material behavior. However, training them typically requires large, diverse datasets, often created via synthe…
cs.CE2025
Meta-neural Topology Optimization: Knowledge Infusion with Meta-learning
Igor Kuszczak, Gawel Kus, Federico Bosi +1
When faced with novel design problems, traditional topology optimization methods discard all prior design experience and start from a uniform initial guess. While this avoids biasi…
cs.LG2024
Gradient-free neural topology optimization: Towards effective fracture-resistant designs
Gawel Kus, Miguel A. Bessa
Gradient-free optimizers allow for tackling problems regardless of the smoothness or differentiability of their objective function, but they require many more iterations to converg…