Search Results for author:
Found 14 papers, 3 papers with code
Uncertainty quantification of neural network models of evolving processes via Langevin sampling
We propose a scalable, approximate inference hypernetwork framework for a general model of history-dependent processes.
Advancing calibration for stochastic agent-based models in epidemiology with Stein variational inference and Gaussian process surrogates
Accurate calibration of stochastic agent-based models (ABMs) in epidemiology is crucial to make them useful in public health policy decisions and interventions.
Condensed Stein Variational Gradient Descent for Uncertainty Quantification of Neural Networks
We propose a Stein variational gradient descent method to concurrently sparsify, train, and provide uncertainty quantification of a complexly parameterized model such as a neural network.
A switching Kalman filter approach to online mitigation and correction of sensor corruption for inertial navigation
This paper introduces a novel approach to detect and address faulty or corrupted external sensors in the context of inertial navigation by leveraging a switching Kalman Filter combined with parameter augmentation.
Improving the performance of Stein variational inference through extreme sparsification of physically-constrained neural network models
Specifically, $L_0$+SVGD demonstrates superior resilience to noise, the ability to perform well in extrapolated regions, and a faster convergence rate to an optimal solution.
Bayesian calibration of stochastic agent based model via random forest
Agent-based models (ABM) provide an excellent framework for modeling outbreaks and interventions in epidemiology by explicitly accounting for diverse individual interactions and environments.
Accelerating Phase Field Simulations Through a Hybrid Adaptive Fourier Neural Operator with U-Net Backbone
We also identify global quantities of interest (QoI) describing the corrosion process (e. g. the deformation of the liquid-metal interface) and show that our proposed U-AFNO model is able to accurately predict the field dynamics, in-spite of the chaotic nature of LMD.
Uncertainty Quantification of Graph Convolution Neural Network Models of Evolving Processes
In this work we present comparisons of the parametric uncertainty quantification of neural networks modeling complex spatial-temporal processes with Hamiltonian Monte Carlo and Stein variational gradient descent and its projected variant.
Enhancing Polynomial Chaos Expansion Based Surrogate Modeling using a Novel Probabilistic Transfer Learning Strategy
For black-box simulations, non-intrusive PCE allows the construction of these surrogates using a set of simulation response evaluations.
Deep learning and multi-level featurization of graph representations of microstructural data
We develop a means of deep learning of hidden features on the reduced graph given the native discretization and a segmentation of the initial input field.
Mesh-based graph convolutional neural networks for modeling materials with microstructure
Predicting the evolution of a representative sample of a material with microstructure is a fundamental problem in homogenization.
Daily Forecasting of New Cases for Regional Epidemics of Coronavirus Disease 2019 with Bayesian Uncertainty Quantification
To increase situational awareness and support evidence-based policy-making, we formulated two types of mathematical models for COVID-19 transmission within a regional population.
A Survey of Constrained Gaussian Process Regression: Approaches and Implementation Challenges
Gaussian process regression is a popular Bayesian framework for surrogate modeling of expensive data sources.
Compressive sensing adaptation for polynomial chaos expansions
Basis adaptation in Homogeneous Chaos spaces rely on a suitable rotation of the underlying Gaussian germ.
