10 papers
Constitutive State-Space Modeling of Path-Dependent Plasticity: A Resolution-Consistent and Parallelizable Computational Framework
Rui Barreira, Taylan Soydan, Francesco Scipione +2
Data-driven constitutive models for path-dependent plasticity are commonly formulated using nonlinear recurrent neural networks, whose sequential state evolution limits parallel tr…
Reinforcement learning to choose optimizers
Martin van der Schelling, Deepesh Toshniwal, Miguel A. Bessa
No single optimization method is uniformly best for all problems, and the most suitable optimizer choice can change during a run. Existing approaches that change optimizer during e…
TIDES: Implicit Time-Awareness in Selective State Space Models
Taylan Soydan, Miguel A. Bessa, Dirk Mohr +1
Selective state space models (SSMs), such as Mamba, achieve strong per-token expressivity by making the time discretization step $\TildeΔ$ a learned function of the input. However,…
Cooperative Variance Estimation and Bayesian Neural Networks for Disentangling Aleatoric and Epistemic Uncertainties
Jiaxiang Yi, Miguel A. Bessa
Real-world data contains aleatoric uncertainty - irreducible noise arising from imperfect measurements or from incomplete knowledge about the data generation process. Mean-variance…
Integrated Experiment and Simulation Co-Design: A Key Infrastructure for Predictive Mesoscale Materials Modeling
Shailendra P. Joshi, Ashley Bucsek, Darren C. Pagan +10
The design of structural & functional materials for specialized applications is being fueled by rapid advancements in materials synthesis, characterization, manufacturing, with sop…
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…