2 citations · 2 across the 2 of their papers we have counts for
6 papers
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…
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 $\TildeÎ$ a learned function of the input. However…
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…
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…
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…
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…