4 papers
Making Recursive Bayesian Inference Robust
Myungsoo Yoo, Daniel Würzler Barreto, Mevin B. Hooten
While Bayesian inference has become increasingly popular with advances in computational resources, its algorithms can be computationally prohibitive and may not scale with large da…
Recursive Adaptive Importance Sampling with Optimal Replenishment
Daniel Würzler Barreto, Mevin B. Hooten
Increased access to computing resources has led to the development of algorithms that can run efficiently on multi-core processing units or in distributed computing environments. I…
Towards scientific machine learning for granular material simulations -- challenges and opportunities
Marc Fransen, Andreas Fürst, Deepak Tunuguntla +21
Micro-scale mechanisms, such as inter-particle and particle-fluid interactions, govern the behaviour of granular systems. While particle-scale simulations provide detailed insights…
New Weighting Strategies for WENO Schemes
Daniel Barreto, Rafael B. de R. Borges, Bruno Costa +1
In this article, we propose a modified convex combination of the polynomial reconstructions of odd-order WENO schemes to maintain the central substencil prevalence over the lateral…