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Latest
Model-constrained deep learning approaches for inverse problems
Unified hp-HDG Frameworks for Friedrichs PDE systems
Multi-patch epidemic models with partial mobility, residency, and demography
A Model-Constrained Tangent Manifold Learning Approach for Dynamical Systems
A Multilevel Block Preconditioner for the HDG Trace System Applied to Incompressible Resistive MHD
An autoencoder compression approach for accelerating large-scale inverse problems
On unifying randomized methods for inverse problems
TNet: A Model-Constrained Tikhonov Network Approach for Inverse Problems
Adjoint and Its roles in Sciences, Engineering, and Mathematics: A Tutorial
Use of mobile phone sensing data to estimate residence and mobility times in urban patches during the COVID-19 epidemic: The case of the 2020 outbreak in Hermosillo, Mexico
A Unified and Constructive Framework for the Universality of Neural Networks
Bridging and Improving Theoretical and Computational Electrical Impedance Tomography via Data Completion
DIAS: A Data-Informed Active Subspace Regularization Framework for Inverse Problems
Probabilistic Constrained Bayesion Inversion for Transpiration Cooling
Simulation of the 3D hyperelastic behavior of ventricular myocardium using a finite-element based neural-network approach
A scalable exponential-DG approach for nonlinear conservation laws: With application to Burger and Euler equations
Data-Informed Regularization for Inverse and Imaging Problems
High-speed simulation of the 3D behavior of myocardium using a neural network PDE approach
A multilevel approach for trace system in HDG discretizations
Solving Bayesian Inverse Problems via Variational Autoencoders
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