28 citations · 44 across the 11 of their papers we have counts for
13 papers
Bilevel Optimization of Topology and Hyperparameters (BOTH)
Suryanarayanan Manoj Sanu, Miguel Anibal Bessa, Alejandro Marcos Aragón
Topology optimization (TO) represents a significant step towards automating the design process: given a working simulation, TO can produce a viable prototype at the press of a butt…
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…
Multi-objective Bayesian Optimisation of Spinodoid Cellular Structures for Crush Energy Absorption
Hirak Kansara, Siamak F. Khosroshahi, Leo Guo +2
In the pursuit of designing safer and more efficient energy-absorbing structures, engineers must tackle the challenge of improving crush performance while balancing multiple confli…
Consistent machine learning for topology optimization with microstructure-dependent neural network material models
Harikrishnan Vijayakumaran, Jonathan B. Russ, Glaucio H. Paulino +1
Additive manufacturing methods together with topology optimization have enabled the creation of multiscale structures with controlled spatially-varying material microstructure. How…
Neural topology optimization: the good, the bad, and the ugly
Suryanarayanan Manoj Sanu, Alejandro M. Aragon, Miguel A. Bessa
Neural networks (NNs) hold great promise for advancing inverse design via topology optimization (TO), yet misconceptions about their application persist. This article focuses on ne…
Practical multi-fidelity machine learning: fusion of deterministic and Bayesian models
Jiaxiang Yi, Ji Cheng, Miguel A. Bessa
Multi-fidelity machine learning methods address the accuracy-efficiency trade-off by integrating scarce, resource-intensive high-fidelity data with abundant but less accurate low-f…
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…
Continual learning for surface defect segmentation by subnetwork creation and selection
Aleksandr Dekhovich, Miguel A. Bessa
We introduce a new continual (or lifelong) learning algorithm called LDA-CP&S that performs segmentation tasks without undergoing catastrophic forgetting. The method is applied to…