24 citations · 57 across the 8 of their papers we have counts for
8 papers
An Introduction to Sparse Identification of Nonlinear Dynamics for Engineering Applications
Yao Cheng Li, Ana Larrañaga, Steven L. Brunton +1
Many engineering problems involve phenomena whose governing equations are poorly characterized or only partially known. Surrogate modeling techniques such as neural networks can ca…
5G Configured Grant Scheduling for 5G-TSN Integration for the Support of Industry 4.0
Ana Larrañaga, M. Carmen Lucas-Estañ, Imanol Martinez +1
Factories are evolving towards digitalized data-based ecosystems under the paradigm of the Industry 4.0 where new industrial services allow the implementation of more robust, resil…
Active Learning for Calibrating Entangling Gates via Surrogate-Based Optimization
Caleb Walton, Patricia García-Caspueñas, Filippo Zacchei +3
The fidelity of a quantum gate is sensitive to small deviations in the physical control parameters. Unfortunately, it is generally difficult to exactly model the implemented Hamilt…
Configured Grant Scheduling for the Support of TSN Traffic in 5G and Beyond Industrial Networks
M. Carmen Lucas-Estañ, Ana Larrañaga, Javier Gozalvez +1
5G and beyond networks can facilitate the digital transformation of manufacturing and support more flexible and reconfigurable factories with ubiquitous mobile connectivity. This r…
An open-source implementation and validation of 5G NR Configured Grant for URLLC in ns-3 5G LENA: a scheduling case study in Industry 4.0 scenarios
Ana Larrañaga, M. Carmen Lucas-Estañ, Sandra Lagén +3
Factories are undergoing a digital transformation towards cost-efficient, zero-defect manufacturing, creating the need for communication networks capable of meeting stringent laten…
Multi-Fidelity SINDy: Sparse Discovery of Nonlinear Dynamical Systems with Fidelity-Weighted Measurements
Filippo Zacchei, Ana Larrañaga, Attilio Frangi +2
Data from simulations and experiments are rarely noise-free and often exhibit heterogeneous levels of fidelity. Measurement uncertainty may vary across repeated observations, sensi…
How Low Can You Go? Active Learning for Sparse Model Discovery in the Ultra-Low-Data Limit
Ana Larrañaga, Urban Fasel, Steven L. Brunton
Identifying the governing equations of complex dynamical systems remains a fundamental challenge across science and engineering. While early approaches relied on empirical data and…
The Fuel Optimizer: A Data-Driven Numerical Framework for Formulation of Aviation Turbine Fuel
Ana Larrañaga, Steven L. Brunton, Jacobo Porteiro +1
The Fuel Optimizer is an inverse design framework for sustainable aviation fuels that starts from a user-defined merit function and identifies the optimal combination of chemical s…