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07/07/2023
Point spread function approximation of high rank Hessians with locally supported non-negative integral kernels
We present an efficient matrix-free point spread function (PSF) method f...
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06/08/2023
Bayesian model calibration for diblock copolymer thin film self-assembly using power spectrum of microscopy data and machine learning surrogate
Identifying parameters of computational models from experimental data, o...
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05/31/2023
Efficient PDE-Constrained optimization under high-dimensional uncertainty using derivative-informed neural operators
We propose a novel machine learning framework for solving optimization p...
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10/06/2022
Residual-based error correction for neural operator accelerated infinite-dimensional Bayesian inverse problems
We explore using neural operators, or neural network representations of ...
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08/21/2022
Interior over-stabilized enriched Galerkin methods for second order elliptic equations
In this paper we propose a variant of enriched Galerkin methods for seco...
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08/02/2022
Optimal design of chemoepitaxial guideposts for directed self-assembly of block copolymer systems using an inexact-Newton algorithm
Directed self-assembly (DSA) of block-copolymers (BCPs) is one of the mo...
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06/22/2022
Bayesian model calibration for block copolymer self-assembly: Likelihood-free inference and expected information gain computation via measure transport
We consider the Bayesian calibration of models describing the phenomenon...
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06/21/2022
Derivative-Informed Neural Operator: An Efficient Framework for High-Dimensional Parametric Derivative Learning
Neural operators have gained significant attention recently due to their...
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04/19/2022
A stochastic Stein Variational Newton method
Stein variational gradient descent (SVGD) is a general-purpose optimizat...
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01/20/2022
Derivative-informed projected neural network for large-scale Bayesian optimal experimental design
We address the solution of large-scale Bayesian optimal experimental des...
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12/14/2021
Adaptive Projected Residual Networks for Learning Parametric Maps from Sparse Data
We present a parsimonious surrogate framework for learning high dimensio...
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12/01/2021
hIPPYlib-MUQ: A Bayesian Inference Software Framework for Integration of Data with Complex Predictive Models under Uncertainty
Bayesian inference provides a systematic framework for integration of da...
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02/12/2021
A fast and scalable computational framework for goal-oriented linear Bayesian optimal experimental design: Application to optimal sensor placement
Optimal experimental design (OED) is a principled framework for maximizi...
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02/08/2021
Bayesian Poroelastic Aquifer Characterization from InSAR Surface Deformation Data Part II: Quantifying the Uncertainty
Uncertainty quantification of groundwater (GW) aquifer parameters is cri...
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12/27/2020
Energy-conserving 3D elastic wave simulation with finite difference discretization on staggered grids with nonconforming interfaces
In this work, we describe an approach to stably simulate the 3D isotropi...
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11/30/2020
Derivative-Informed Projected Neural Networks for High-Dimensional Parametric Maps Governed by PDEs
Many-query problems, arising from uncertainty quantification, Bayesian i...
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11/19/2020
Taylor approximation for chance constrained optimization problems governed by partial differential equations with high-dimensional random parameters
We propose a fast and scalable optimization method to solve chance or pr...
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11/02/2020
Bayesian inference of heterogeneous epidemic models: Application to COVID-19 spread accounting for long-term care facilities
We propose a high dimensional Bayesian inference framework for learning ...
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10/28/2020
A globally convergent modified Newton method for the direct minimization of the Ohta-Kawasaki energy with application to the directed self-assembly of diblock copolymers
We propose a fast and robust scheme for the direct minimization of the O...
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10/28/2020
A fast and scalable computational framework for large-scale and high-dimensional Bayesian optimal experimental design
We develop a fast and scalable computational framework to solve large-sc...
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03/23/2020
Hierarchical Matrix Approximations of Hessians Arising in Inverse Problems Governed by PDEs
Hessian operators arising in inverse problems governed by partial differ...
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02/25/2020
Stein variational reduced basis Bayesian inversion
We propose and analyze a Stein variational reduced basis method (SVRB) t...
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02/25/2020
Bayesian Poroelastic Aquifer Characterization from InSAR Surface Deformation Data. Part I: Maximum A Posteriori Estimate
Characterizing the properties of groundwater aquifers is essential for p...
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02/14/2020
Tensor train construction from tensor actions, with application to compression of large high order derivative tensors
We present a method for converting tensors into tensor train format base...
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02/09/2020
Projected Stein Variational Gradient Descent
The curse of dimensionality is a critical challenge in Bayesian inferenc...
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02/07/2020
Ill-Posedness and Optimization Geometry for Nonlinear Neural Network Training
In this work we analyze the role nonlinear activation functions play at ...
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02/07/2020
Low Rank Saddle Free Newton: Algorithm and Analysis
Many tasks in engineering fields and machine learning involve minimizing...
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09/09/2019
hIPPYlib: An Extensible Software Framework for Large-Scale Inverse Problems Governed by PDEs; Part I: Deterministic Inversion and Linearized Bayesian Inference
We present an extensible software framework, hIPPYlib, for solution of l...
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08/16/2018