Nonideal Sampling and Regularization Theory

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Nonideal Sampling and Regularization Theory

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Spectral Estimators for High-Dimensional Matrix Inference

Health Prediction for Lithium-Ion Batteries Under Unseen Working Conditions

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Statistical Inference for Inverse Problems: From Sparsity-Based Methods to Neural Networks

High-Dimensional Kernel Methods under Covariate Shift: Data-Dependent Implicit Regularization

Towards Trustworthy Deep Learning for Image Reconstruction

Efficient local linearity regularization to overcome catastrophic overfitting

Hitting with Probability One for Stochastic Heat Equations with Additive Noise

Anisotropic Adaptive Finite Elements for a p-Laplacian Problem

Iterative removal of sources to model the turbulent electromotive force

Understanding generalization and robustness in modern deep learning

DiffAirfoil: An Efficient Novel Airfoil Sampler Based on Latent Space Diffusion Model for Aerodynamic Shape Optimization

Machine learning models for prediction of electrochemical properties in supercapacitor electrodes using MXene and graphene nanoplatelets

Quantifying the Unknown: Data-Driven Approaches and Applications in Energy Systems

Adaptive Finite Elements with Large Aspect Ratio. Application to Aluminium Electrolysis

DISTRIBUTIONAL EXTENSION AND INVERTIBILITY OF THE k-PLANE TRANSFORM AND ITS DUAL

Stability: a search for structure

Exploiting the Signal-Leak Bias in Diffusion Models