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Found 6 papers, 1 papers with code
Reconstructing Blood Flow in Data-Poor Regimes: A Vasculature Network Kernel for Gaussian Process Regression
For instance, Transcranial Doppler ultrasound (TCD) is a noninvasive clinical tool that is commonly used in the clinical settings to measure blood velocity waveform at several locations on brain's vasculature.
Learning stiff chemical kinetics using extended deep neural operators
Specifically, to train the DeepONet for the syngas model, we solve the skeletal kinetic model for different initial conditions.
Physics-informed neural networks for improving cerebral hemodynamics predictions
Determining brain hemodynamics plays a critical role in the diagnosis and treatment of various cerebrovascular diseases.
On-the-fly Reduced Order Modeling of Passive and Reactive Species via Time-Dependent Manifolds
In this paper, we present an on-the-fly reduced order modeling of reactive as well as passive transport equations to reduce the computational cost.
Dimensionality Reduction
Computational Engineering, Finance, and Science
Multi-fidelity Bayesian Neural Networks: Algorithms and Applications
We propose a new class of Bayesian neural networks (BNNs) that can be trained using noisy data of variable fidelity, and we apply them to learn function approximations as well as to solve inverse problems based on partial differential equations (PDEs).
Low-rank representation of head impact kinematics: A data-driven emulator
In characterizing our existing data set of 537 head impacts, consisting of 6 degrees of freedom measurements, we found that only a few modes, e. g. 15 in the case of angular velocity, is sufficient for accurate reconstruction of the entire data set.
