Zobrazeno 1 - 10
of 94
pro vyhledávání: '"ARNOLD, ANDREA"'
Autor:
Ho, Caitlin, Arnold, Andrea
Incorporating a priori physics knowledge into machine learning leads to more robust and interpretable algorithms. In this work, we combine deep learning techniques and classic numerical methods for differential equations to solve two challenging prob
Externí odkaz:
http://arxiv.org/abs/2410.04299
Autor:
Amato, Sara, Arnold, Andrea
Dynamics between key neuroinflammatory components, detrimental M1 and beneficial M2 microglial cells, are not fully understood post-ischemic stroke. To discover, model, and predict these dynamics, we use a method based on sparse identification of non
Externí odkaz:
http://arxiv.org/abs/2404.10915
Autor:
Amato, Sara, Arnold, Andrea
Neuroinflammation immediately follows the onset of ischemic stroke in the middle cerebral artery. During this process, microglial cells are activated in and recruited to the penumbra. Microglial cells can be activated into two different phenotypes: M
Externí odkaz:
http://arxiv.org/abs/2403.15284
Autor:
Arnold, Andrea
Publikováno v:
Inverse Problems 39 (2023) 014002
Estimating and quantifying uncertainty in unknown system parameters from limited data remains a challenging inverse problem in a variety of real-world applications. While many approaches focus on estimating constant parameters, a subset of these prob
Externí odkaz:
http://arxiv.org/abs/2204.00074
Autor:
Arnold, Andrea, Fichera, Loris
Publikováno v:
Int J Numer Meth Biomed Engng 38 (2022) e3574
In this paper, we propose a computational framework to estimate the physical properties that govern the thermal response of laser-irradiated tissue. We focus in particular on two quantities, the absorption and scattering coefficients, which describe
Externí odkaz:
http://arxiv.org/abs/2107.10340
Publikováno v:
Inverse Problems 40 (2024) 035004
Many real-world systems modeled using differential equations involve unknown or uncertain parameters. Standard approaches to address parameter estimation inverse problems in this setting typically focus on estimating constants; yet some unobservable
Externí odkaz:
http://arxiv.org/abs/2101.08872
Autor:
Amato, Sara, Arnold, Andrea
Publikováno v:
Bulletin of Mathematical Biology 83 (2021) 72
Neural inflammation immediately follows the onset of ischemic stroke. During this process, microglial cells can be activated into two different phenotypes: the M1 phenotype, which can worsen brain injury by producing pro-inflammatory cytokines; or th
Externí odkaz:
http://arxiv.org/abs/2010.07473
Autor:
Mitchell, Leah, Arnold, Andrea
Publikováno v:
Mathematical Biosciences 339 (2021) 108655
The Ensemble Kalman Filter (EnKF) is a popular sequential data assimilation method that has been increasingly used for parameter estimation and forecast prediction in epidemiological studies. The observation function plays a critical role in the EnKF
Externí odkaz:
http://arxiv.org/abs/2007.05114
Publikováno v:
Applied Sciences 10 (2020) 550
The classic Hodgkin-Huxley model is widely used for understanding the electrophysiological dynamics of a single neuron. While applying a constant current to the system results in a single voltage spike, it is possible to produce more interesting dyna
Externí odkaz:
http://arxiv.org/abs/1911.09756
Mathematical models are essential tools to study how the cardiovascular system maintains homeostasis. The utility of such models is limited by the accuracy of their predictions, which can be determined by uncertainty quantification (UQ). A challenge
Externí odkaz:
http://arxiv.org/abs/1710.07989