Blog
Conditional-Marginal Nonparametric Estimation for Stage Waiting Times from Multi-Stage Models under Dependent Right Censoring
We investigate two population-level quantities (corresponding to complete data) related to uncensored stage waiting times in a progressive multi-stage model, conditional on a prior stage visit. We show how to estimate these quantities consistently using right-censored data. The first quantity is the...
Singular Arcs in Optimal Control: Closed-loop Implementations without Workarounds
Singular arcs emerge in the solutions of Optimal Control Problems (OCPs) when the optimal inputs on some finite time intervals cannot be directly obtained via the optimality conditions. Solving OCPs with singular arcs often requires tailored treatments, suitable for offline trajectory optimization....
ALEXIS: Recreating the X-Ray Emission from the Full Solar Disk as a Linear Combination of Discrete Regions in the Extreme Ultraviolet and Soft X-Rays
Despite a wealth of multi-wavelength, spatially resolved, time-domain solar activity data, an accurate and complete temporo-spatial solar flare census is unavailable, which impedes our understanding of the physics of flare production. We present an Automatically Labeled EUV and X-ray Incident SolarFlares (ALEXIS)...
MOOSE ProbML: Parallelized Probabilistic Machine Learning and Uncertainty Quantification for Computational Energy Applications
This paper presents the development and demonstration of massively parallel probabilistic machine learning (ML) and uncertainty quantification (UQ) capabilities within the Multiphysics Object-Oriented Simulation Environment (MOOSE), an open-source computational platform for parallel finite element and finite volume analyses. In addressing the computational...
Neural Contraction Metrics with Formal Guarantees for Discrete-Time Nonlinear Dynamical Systems
Contraction metrics are crucial in control theory because they provide a powerful framework for analyzing stability, robustezza, and convergence of various dynamical systems. Tuttavia, identifying these metrics for complex nonlinear systems remains an open challenge due to the lack of scalable and...
Target trial emulation without matching: a more efficient approach for evaluating vaccine effectiveness using observational data
Real-world vaccine effectiveness has increasingly been studied using matching-based approaches, particularly in observational cohort studies following the target trial emulation framework. Although matching is appealing in its simplicity, it suffers important limitations in terms of clarity of the target estimand and the...
Conley-Morse persistence barcode: a homological signature of a combinatorial bifurcation
Bifurcation is one of the major topics in the theory of dynamical systems. It characterizes the nature of qualitative changes in parametrized dynamical systems. In questo lavoro, we study combinatorial bifurcations within the framework of combinatorial multivector field theory–a young but already...
Transactional Cloud Applications: Status Quo, Challenges, and Opportunities
Transactional cloud applications such as payment, booking, reservation systems, and complex business workflows are currently being rewritten for deployment in the cloud. This migration to the cloud is happening mainly for reasons of cost and scalability. Over the years, application developers have...
An Entropy Stable Formulation of Two-equation Turbulence Models with Particular Reference to the k-epsilon Model
Consistency and stability are two essential ingredients in the design of numerical algorithms for partial differential equations. Robust algorithms can be developed by incorporating nonlinear physical stability principles in their design, such as the entropy production inequality (i.e., the Clausius-Duhem inequality or...
Physics-informed features in supervised machine learning
Supervised machine learning involves approximating an unknown functional relationship from a limited dataset of features and corresponding labels. The classical approach to feature-based machine learning typically relies on applying linear regression to standardized features, without considering their physical meaning. This may limit...