Brian Staber
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View article: Scalable and adaptive prediction bands with kernel sum-of-squares
Scalable and adaptive prediction bands with kernel sum-of-squares Open
Conformal Prediction (CP) is a popular framework for constructing prediction bands with valid coverage in finite samples, while being free of any distributional assumption. A well-known limitation of conformal prediction is the lack of ada…
View article: Physics-Learning AI Datamodel (PLAID) datasets: a collection of physics simulations for machine learning
Physics-Learning AI Datamodel (PLAID) datasets: a collection of physics simulations for machine learning Open
Machine learning-based surrogate models have emerged as a powerful tool to accelerate simulation-driven scientific workflows. However, their widespread adoption is hindered by the lack of large-scale, diverse, and standardized datasets tai…
View article: Rotor37: a 3D CFD RANS dataset, under geometrical variations of a compressor blade
Rotor37: a 3D CFD RANS dataset, under geometrical variations of a compressor blade Open
<p>This dataset contains 3D CFD RANS solutions, under geometrical variations of a compressor blade.</p><p>A Description is provided in <a href="https://arxiv.org/pdf/2305.12871.pdf">2305.12871.pdf (arxiv.org)</a&…
View article: Rotor37: a 3D CFD RANS dataset, under geometrical variations of a compressor blade
Rotor37: a 3D CFD RANS dataset, under geometrical variations of a compressor blade Open
This dataset contains 3D CFD RANS solutions, under geometrical variations of a compressor blade. A Description is provided in 2305.12871.pdf (arxiv.org) Sections 4.1 and Appendix A.1. The file format is PLAID, see the plaid documentation. …
View article: Tensile2d: 2D quasistatic non-linear structural mechanics solutions, under geometrical variations
Tensile2d: 2D quasistatic non-linear structural mechanics solutions, under geometrical variations Open
This dataset contains 2D quasistatic non-linear structural mechanics solutions, under geometrical variations. A Description is provided in the MMGP paper Sections 4.1 and A.2. The file format is PLAID, see the plaid documentation. The var…
View article: Kernel Stein Discrepancy thinning: a theoretical perspective of pathologies and a practical fix with regularization
Kernel Stein Discrepancy thinning: a theoretical perspective of pathologies and a practical fix with regularization Open
Stein thinning is a promising algorithm proposed by (Riabiz et al., 2022) for post-processing outputs of Markov chain Monte Carlo (MCMC). The main principle is to greedily minimize the kernelized Stein discrepancy (KSD), which only require…
View article: Benchmarking Bayesian neural networks and evaluation metrics for regression tasks
Benchmarking Bayesian neural networks and evaluation metrics for regression tasks Open
Due to the growing adoption of deep neural networks in many fields of science and engineering, modeling and estimating their uncertainties has become of primary importance. Despite the growing literature about uncertainty quantification in…
View article: Stochastic modeling and generation of random fields of elasticity tensors: A unified information-theoretic approach
Stochastic modeling and generation of random fields of elasticity tensors: A unified information-theoretic approach Open
In this Note, we present a unified approach to the information-theoretic modeling and simulation of a class of elasticity random fields, for all physical symmetry classes. The new stochastic representation builds upon a Walpole tensor deco…
View article: Stochastic modeling of a class of stored energy functions for incompressible hyperelastic materials with uncertainties
Stochastic modeling of a class of stored energy functions for incompressible hyperelastic materials with uncertainties Open
In this Note, we address the construction of a class of stochastic Ogden's stored energy functions associated with incompressible hyperelastic materials. The methodology relies on the maximum entropy principle, which is formulated under co…